WO2005118851A1 - Oligonucleotides for cancer diagnosis - Google Patents

Abstract

The present invention provides sets of oligonucleotides corresponding to genes encoding proteins involved in protein synthesis and/or stability or genes encoding proteins involved in the regulation of defence and/or chromatin remodelling for use in preparing transcript patterns particularly for cancer diagnosis. The invention also extends to such sets and kits containing such sets as well as related methods reliant on analysis of marker polypeptides encoded by the genes to develop characteristic expression profiles.

Description

OLIGONUCLEOTIDES FOR CANCER DIAGNOSIS

The present invention relates to oligonucleotide probes, for use in assessing gene transcript levels in a cell, which may be used in analytical techniques, particularly diagnostic techniques. Conveniently the probes are provided in kit form. Different sets of probes may be used in techniques to prepare gene expression patterns and identify, diagnose or monitor different cancers or stages thereof. The identification of quick and easy methods of sample analysis for, for example, diagnostic applications, remains the goal of many researchers. End users seek methods which are cost effective, produce statistically significant results and which may be implemented routinely without the need for highly skilled individuals. The analysis of gene expression within cells has been used to provide information on the state of those cells and importantly the state of the individual from which the cells are derived. The relative expression of various genes in a cell has been identified as reflecting a particular state within a body. For example, cancer cells are known to exhibit altered expression of various proteins and the transcripts or the expressed proteins may therefore be used as markers of that disease state. Thus biopsy tissue may be analysed for the presence of these markers and cells originating from the site of the disease may be identified in other tissues or fluids of the body by the presence of the markers. Furthermore, products of the altered expression may be released into the blood stream and these products may be analysed. In addition cells which have contacted disease cells may be affected by their direct contact with those cells resulting in altered gene expression and their expression or products of expression may be similarly analysed. However, there are some limitations with these methods. For example, the use of specific tumour markers for identifying cancer suffers from a variety' of defects, such as lack of specificity or sensitivity, association of the marker with disease states besides the specific type of cancer, and difficulty of detection in asymptomatic individuals. In addition to the analysis of one or two marker transcripts or proteins, more recently, gene expression patterns have been analysed. Most of the work involving large-scale gene expression analysis with implications in disease diagnosis has involved clinical samples originating from diseased tissues or cells. For example, several recent publications, which demonstrate that gene expression data can be used to distinguish between similar cancer types, have used clinical samples from diseased tissues or cells (Alon et al. 1999, PNAS, 96, p6745-6750; Golub et al. 1999, Science, 286, p531 -537; Alizadeh et al, 2000,

Nature, 403, p503-511; Bittner et al., 2000, Nature, 406, p536-540). However, these methods have relied on analysis of a sample containing diseased cells or products of those cells or cells which have been contacted by disease cells. Analysis of such samples relies on knowledge of the presence of a disease and its location, which may be difficult in asymptomatic patients.

Furthermore, samples can not always be taken from the disease site, e.g. in diseases of the brain. In a finding of great significance, the present inventors identified the previously untapped potential of all cells within a body to provide information relating to the state of the organism from which the cells were derived.

WO98/49342 describes the analysis of the gene expression of cells distant from the site of disease, e.g. peripheral blood collected distant from a cancer site. PCT/GB03/005102, incoφorated herein by reference, describes specific probes for the diagnosis of breast cancer and Alzheimer's disease and discusses protocols for identifying other appropriate probes for that purpose and for diagnosing other diseases. This finding is based on the premise that the different parts of an organism's body exist in dynamic interaction with each other. When a disease affects one part of the body, other parts of the body are also affected. The interaction results from a wide spectrum of biochemical signals that are released from the diseased area, affecting other areas in the body. Although, the nature of the biochemical and physiological changes induced by the released signals can vary in the different body parts, the changes can be measured at the level of gene expression and used for diagnostic purposes. The physiological state of a cell in an organism is determined by the pattern with which genes are expressed in it. The pattern depends upon the internal and external biological stimuli to which said cell is exposed, and any change either in the extent or in the nature of these stimuli can lead to a change in the pattern with which the different genes are expressed in the cell. There is a growing understanding that by analysing the systemic changes in gene expression patterns in cells in biological samples, it is possible to provide information on the type and nature of the biological stimuli that are acting on them. Thus, for example, by monitoring the expression of a large number of genes in cells in a test sample, it is possible to determine whether their genes are expressed with a pattern characteristic for a particular disease, condition or stage thereof. Measuring changes in gene activities in cells, e.g. from tissue or body fluids is therefore emerging as a powerful tool for disease diagnosis. Such methods have various advantages. Often, obtaining clinical samples from certain areas in the body that is diseased can be difficult and may involve undesirable invasions in the body, for example biopsy is often used to obtain samples for cancer. In some cases, such as in Alzheimer's disease the diseased brain specimen can only be obtained post-mortem. Furthermore, the tissue specimens which are obtained are often heterogeneous and may contain a mixture of both diseased and non-diseased cells, making the analysis of generated gene expression data both complex and difficult. It has been suggested that a pool of tumour tissues that appear to be pathogenetically homogeneous with respect to moφhological appearances of the tumour may well be highly heterogeneous at the molecular level (Alizadeh, 2000, supra), and in fact might contain tumours representing essentially different diseases (Alizadeh, 2000, supra; Golub, 1999, supra). For the pmpose of identifying a disease, condition, or a stage thereof, any method that does not require clinical samples to originate directly from diseased tissues or cells is highly desirable since clinical samples representing a homogeneous mixture of cell types can be obtained from an easily accessible region in the body. We have now identified a family of sequences which allow the derivation of a set of probes of suφrising utility for identifying cancer, particularly breast cancer. Thus, we now describe families of genes whose expression is altered in the cells of blood samples from cancer patients, which may be used to generate probes for use in methods of identifying, diagnosing or monitoring cancer or stages thereof. In work leading up to this invention, the inventors examined the level of expression of a large number of genes in cancer patients relative to normal patients. Not only were a large number of genes found to exhibit altered expression, but, in addition, those which exhibited altered expression were found to fall within discrete families of genes, by virtue of their function. As such these genes provide a pool from which corresponding probes may be generated which can be used collectively to generate a frngeφrint of the expression of these genes in an individual. Since the expression of these genes is altered in the cancer individual, and may hence be considered informative for that state, the generated fingeφrint from the collection of probes is indicative of the disease relative to the normal state. The families of genes that have been identified as being differentially expressed in cancer patients may be summarized as follows: (i) genes encoding proteins involved in protein synthesis and/or stability; (ii) genes encoding proteins involved in the regulation of defence and/or chromatin remodelling. Family (i) includes: (a) genes encoding ribosomal proteins and ribosomal activation proteins (ie. proteins comprising components of ribosomal proteins or involved in modification of their function and are found to be down-regulated in cancer patients). These encoded proteins include ribosomal proteins Ll- L56, L7A, L10A, L13A, L18A, L23A, L27A, L35A, L36A, L37A, PO, PI, P2, S2-S29, S31, S33-S36, S3A, S15A, S18A, S18B, S18C, S27A, 63, 115 (and pseudogenes), ribosomal protein kinases (e.g. S6 kinase), ribonucleases, putative SI RNA binding domain protein, eukaryotic translation initiation factors and guanine nucleotide binding protein G; (b) genes encoding translation inhibition and initiation factors (ie. proteins involved in the translation of mRNA to a protein product and are found to be down-regulated in cancer patients). These encoded proteins include eukaryotic translation elongation factors, tRNA synthetases, RNA binding proteins, polyadenylation element binding proteins, tyrosine phosphatases, eukaryotic translation initiation factors, and RNA polymerase I, III transcription factors; (c) genes encoding other modulators of transcription or translation such as cyclin D-type binding protein and guanine nucleotide binding protein.

Family (ii) includes: (a) genes encoding immune response related proteins (ie. proteins which are up-regulated in response to immune stimulation, and which include proteins upregulated in response to inflammation or in generating an inflammatory response, and are found to be up- regulated in cancer patients). These encoded proteins include T-cell receptor and associated components, e.g. protein kinases, various cytokines, including the interleukins and their receptors (such as IL-1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17, 18, 20, 22, 24), tumour necrosis factor and its receptor and its superfamily (e.g. TNF superfamily members 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15), interferon regulatory factors, oncostatin M, Leukemia inhibitory factor, chemokine ligand and receptor family (e.g. numbers 1- 28), complement components, interferon stimulated factors such as transcription factors, MHC (e.g HLA) class I or II (or related components) (e.g. DQ, DR, DO, DP, DM alpha or beta), adhesion proteins (e.g. CD1A, CD1C, CD1D, CD3Z, 6, 8, 11, 14, 18, 24, 27, 28, 29, 40, 44, 50, 54, 59, 74, 79B, 80, 81, 83, 86, 96, ICAM), nuclear factor of kappa polypeptide gene enhancer in B-cells, myelin basic protein, cathepsin, toll-like receptor, proteosome subunits, ferritin, protein kinases or phosphatases as well as their activators and inhibitors, leukocyte immunoglobulin-like receptor, immunoglobulin components, e.g. heavy chain or Fc fragments, e.g. of IgG, IgE or IgA or their superfamily, defensin, oxytocin, Si 00 calcium binding protein, lectin and its receptor and superfamily, leptin, phospholipase and growth factors (such as endothelial cell growth factor or erythropoietin);

(b) genes encoding TNF-induced proteins (ie. proteins which are induced in an individual in response to exposure to TNF and are found to be up- regulated in cancer patients). These encoded proteins include TNF alpha-induced protein 8, integrin, inhibitor of kappa light polypeptide gene enhancer in B-cells, TNF-associated factor 2, 5, nuclear factor of kappa light polypeptide gene enhancer in B-cells, MAP kinases, protein kinase C, ubiquitous kinase, cadherin, caspase, cyclin Dl, superoxide dismutase and interleukins;

(c) genes encoding hypoxia-induced proteins (ie. proteins which are induced when the individual or a part thereof is in a state of hypoxia and are found to be up-regulated in cancer patients). These encoded proteins include sestrin, El A binding protein p300, endothelin, ataxia telangiectasia and Rad3 related protein, hexokinase 2, TEK tyrosine kinase, DNA fragmentation factor, caspase, plasminogen activator, hypoxia-inducible factor 1 and glucose phosphate isomerase;

(d) genes encoding oxidative stress proteins (ie. proteins which are induced in an individual or part thereof under oxidative stress and are found to be up-regulated in cancer patients). These encoded proteins include superoxide dismutase, glutathione synthetase, catalase, lactoperoxidase, thyroid peroxidase, myeloperoxidase, eosinophil peroxidase, oxidation resistance 1, peroxiredoxin, cytochrome P450, scavenger receptor, paraoxonase, glutathione reductase, NAD(P)H dehydrogenase, glutathione S-transferase, catenin, glutaredoxin, heat shock proteins (such as heat shock transcription factors), mitogen- activated protein kinases, enolase, thioredoxin reductase and peroxiredoxin; (e) genes encoding proteins involved in chromatin remodelling (ie. proteins which are instrumental in maintaining or modifying chromatin structure and may be essential for gene regulation). These encoded proteins include histone replacement proteins, e.g. H3.3A or H3.3B family. Appropriate gene sequences falling within the families described above may be identified by interrogation of appropriate databases using as keywords the family name, e.g. "immune response" on gene or protein databases at

National Centre for Biotechnology Information, Norway. For confirmation of the utility of such gene sequences for the development of oligonucleotides for the tests described herein, the expression of a particular gene sequence may be assessed in a test cancer patient versus a normal patient. Nariation in expression above or below control levels is indicative of the utility of the sequence for probe derivation. Generally the genes encoding the above (i) families are down-regulated in cancer versus normal patients and in the case of (ii) families the encoding genes are up-regulated. It is speculated that in cancer patients the systematic decreased expression of genes involved in ribosome production and translation control may indicate that blood cells are responding to a new condition in those patients by decreasing the rate of protein synthesis which may be a cellular adaption to an environment of low oxygen and energy deficiency. This is supported by the observation that genes involved in defence against reactive oxygen species

(ROS) such as MnSOD and ferritin are upregulated in cancer samples. Low erythropoietin may explain the low oxygen levels in cancer patients. TΝF activation is also believed to be a route for the changes in the families of genes described above since TΝF is known to up regulate expression of e.g. ferritin, defensin, MnSOD and calgranulin B. TΝF also inhibits EPO production which can itself cause a low oxygen condition in the blood environment. Hypoxia is known to induce TΝF levels. These changes may be triggered by angiogenic factors entering the bloodstream. Although not wishing to be bound by theory, the hypothesis underlying the above described effects is shown in Figure 1. Thus the invention provides a set of oligonucleotide probes which correspond to genes in a cell whose expression is affected in a pattern characteristic of a particular cancer or stage of, wherem said genes are systemically affected by said cancer or stage thereof. Preferably said genes are constitutively moderately or highly expressed. Preferably the genes are moderately or highly expressed in the cells of the sample but not in cells from disease cells or in cells having contacted such disease cells. Such probes, particularly when isolated from cells distant to the site of disease, do not rely on the development of disease to clinically recognizable levels and allow detection of a cancer or stage thereof very early after the onset of said cancer, even years before other subjective or objective symptoms appear. As used herein "systemically" affected genes refers to genes whose expression is affected in the body without direct contact with a disease cell or disease site and the cells under investigation are not disease cells. "Contact" as referred to herein refers to cells coming into close proximity with one another such that the direct effect of one cell on the other may be observed, e.g. an immune response, wherein these responses are not mediated by secondary molecules released from the first cell over a large distance to affect the second cell. Preferably contact refers to physical contact, or contact that is as close as is sterically possible, conveniently, cells which contact one another are found in the same unit volume, for example within 1cm³. A "disease cell" is a cell manifesting phenotypic changes and is present at the disease site at some time during its life-span, e.g. a tumour cell at the tumour site or which has disseminated from the tumour, or a brain cell in the case of cancer of the brain. "Moderately or highly" expressed genes refers to those present in resting cells in a copy number of more than 30-100 copies/cell (assuming an average 3xl0⁵ mRNA molecules in a cell). Specific probes having the above described properties are provided herein. Thus in one aspect, the present invention provides a set of oligonucleotide probes, wherein said set comprises at least 10 oligonucleotides selected from: an oligonucleotide correspondmg to a gene sequence from family (i) or (ii) as defined hereinbefore or derived from such a sequence, or an oligonucleotide with a complementary sequence, or a functionally equivalent oligonucleotide. The invention further provides a method of preparing a set of oligonucleotides for use in the methods described herein, comprising the step of selecting one or more oligonucleotides conesponding to a gene sequence from family (i) and one or more oligonucleotides conesponding to a gene sequence from family (ii). Preferably more than 1 oligonucleotides is selected from each family (e.g. from different sub- families) and the selected oligonucleotides are from preferred genes as described herein. The invention also provides one or more oligonucleotide probes, wherein each oligonucleotide probe is selected from the oligonucleotides listed in Table 2, 3 or 4 (e.g. from Table 2) or derived from a sequence described in Table 2, 3 or 4, or a complementary sequence thereof. Said derived oligonucleotides include oligonucleotides derived from the genes corresponding to the sequences provided in those tables, e.g. the genes set forth in Tables 2, 5 or 6 (see the

Accession numbers), or the complementary sequences thereof. The use of such probes in products and methods of the invention, form further aspects of the invention. As refened to herein an "oligonucleotide" is a nucleic acid molecule having at least 6 monomers in the polymeric structure, ie. nucleotides or modified forms thereof. The nucleic acid molecule may be DNA, RNA or PNA (peptide nucleic acid) or hybrids thereof or modified versions thereof, e.g. chemically modified forms, e.g. LNA (Locked Nucleic acid), by methylation or made up of modified or non-natural bases during synthesis, providing they retain their ability to bind to complementary sequences. Such oligonucleotides are used in accordance with the invention to probe target sequences and are thus referred to herein also as oligonucleotide probes or simply as probes. An oligonucleotide corresponding to a gene sequence from family (i) or (ii) refers to an oligonucleotide conesponding to all or a part of said gene sequence or its transcript. When a part of the gene sequence is used, it satisfies the requirements of the oligonucleotide probes as described herein, e.g. in length and function. Preferably said parts have the size described hereinafter. Said oligonucleotide is referred to hereinafter as the primary oligonucleotide. A derived oligonucleotide refers to an oligonucleotide which is a part of the primary oligonucleotide but satisfies the requirements for probes as described herein. Preferably the oligonucleotide probes forming said set are at least 15 bases in length to allow binding of target molecules. Especially preferably said oligonucleotide probes are from 20 to 200 bases in length, e.g. from 30 to 150 bases, preferably 50-100 bases in length. As refened to herein the term "complementary sequences" refers to sequences with consecutive complementary bases (ie. T:A, G:C) and which complementary sequences are therefore able to bind to one another through their complementarity. Reference to "10 oligonucleotides" refers to 10 different oligonucleotides. Whilst an oligonucleotide from a gene sequence family as described herein, a derived oligonucleotide and their functional equivalent are considered different ohgonucleotides, complementary oligonucleotides are not considered different. Preferably however, the at least 10 oligonucleotides correspond to 10 different gene sequences within the described gene sequence families (or derived oligonucleotides or their functional equivalents). Thus said 10 different oligonucleotides are preferably able to bind to 10 different transcripts. Preferably the at least 10 oligonucleotides are made up of a combination of oligonucleotides from family (i) and (ii), e.g. 5 oligonucleotides from each family may be used, or 4 from one family and 6 from the other family. This advantageously allows the use of genes which are up and down-regulated in cancer relative to normal patients. Conveniently, one or more oligonucleotides from different sub-families may be used, e.g. 2 probes each from (i)a, (i)b, (i)c, (ii)a and (ii)b. Especially preferably said set of oligonucleotides includes oligonucleotides from family (i)a, (ii)a and (ii)e. Preferred proteins encoded by family (i)a genes are ribosomal proteins and preferably each set includes an oligonucleotide from a gene encoding such a protein. Preferred immune response proteins encoded by family (ii)a genes include adhesion proteins, interleukins their receptors and superfamily, TNF its receptor and superfamily, immunoglobulin components and erythropoietin. Particularly preferably said set includes oligonucleotides from genes encoding one or more ribosomal proteins and optionally one or more histones and optionally ferritin. Preferably said ohgonucleotides are as described in Table 2 or 3 or are derived from a sequence described in Table 2 or 3, e.g. as described in Table 2. Said set may additionally comprise one or more oligonucleotide probes listed in Table 4, or derived from a sequence described in Table 4, or a complementary sequence thereof. Said derived oligonucleotides include oligonucleotides derived from the genes conesponding to the sequences provided in those tables, e.g. the genes set forth in Tables 2, 5 or 6 (see the Accession numbers), or the complementary sequences thereof. A "set" as described refers to a collection of unique oligonucleotide probes (ie. having a distinct sequence) and preferably consists of less than 1000 oligonucleotide probes, especially less than 500 probes, e.g. preferably from 10 to 500, e.g. 10 to 100, 200 or 300, especially preferably 20 to 100, e.g. 30 to 100 probes. In some cases less than 10 probes may be used, e.g. from 2 to 9 probes, e.g. 5 to 9 probes. It will be appreciated that increasing the number of probes will prevent the possibility of poor analysis, e.g. misdiagnosis by comparison to other diseases which could similarly alter the expression of the particular genes in question. Other oligonucleotide probes not described herein may also be present, particularly if they aid the ultimate use of the set of oligonucleotide probes. However, preferably said set consists only of the oligonucleotides described herein, or a sub-set thereof (e.g. of the size as described above). Multiple copies of each unique oligonucleotide probe, e.g. 10 or more copies, may be present in each set, but constitute only a single probe. A set of oligonucleotide probes, which may preferably be immobilized on a solid support or have means for such immobihzation, comprises the at least 10 oligonucleotide probes selected from those described hereinbefore. As mentioned above, these 10 probes must be unique and have different sequences. Having said this however, two separate probes may be used which recognize the same gene but reflect different splicing events. However oligonucleotide probes which are complementary to, and bind to distinct genes are preferred. As described herein a "functionally equivalent" or derived oligonucleotide refers to an oligonucleotide which is capable of identifying the same gene as an oligonucleotide from a sequence in the gene sequence families described herein ie. it can bind to the same mRNA molecule (or DNA) transcribed from a gene (target nucleic acid molecule) as the primary oligonucleotide or the derived oligonucleotide (or its complementary sequence).

Thus in a prefened feature said derived or functionally equivalent oligonucleotide is apart of a gene sequence as defined in Table 2, 5 or 6, or the complementary sequence thereof. Preferably said functionally equivalent oligonucleotide is capable of recognizing, ie. binding to the same splicing product as a primary oligonucleotide or a derived oligonucleotide. Preferably said mRNA molecule is the full length mRNA molecule which corresponds to the primary oligonucleotide or the derived oligonucleotide. As refened to herein "capable of binding" or "binding" refers to the ability to hybridize under conditions described hereinafter. Alternatively expressed, functionally equivalent ohgonucleotides (or complementary sequences) have sequence identity or will hybridize, as described hereinafter, to a region of the target molecule to which molecule a primary oligonucleotide or a derived oligonucleotide or a complementary oligonucleotide binds. Preferably, functionally equivalent oligonucleotides (or their complementary sequences) hybridize to one of the mRNA sequences which corresponds to a primary oligonucleotide or a derived oligonucleotide under the conditions described hereinafter or has sequence identity to a art of one of the mRNA sequences which conesponds to a primary oligonucleotide or a derived oligonucleotide. A "part" in this context refers to a stretch of at least 5, e.g. at least 10 or 20 bases, such as from 5 to 100, e.g. 10 to 50 or 15 to 30 bases. In a particularly prefened aspect, the functionally equivalent oligonucleotide binds to all or a part of the region of a target nucleic acid molecule (mRNA or cDNA) to which the primary oligonucleotide or derived oligonucleotide binds. A "target" nucleic acid molecule is the gene transcript or related product e.g. mRNA, or cDNA, or amplified product thereof. Said "region" of said target molecule to which said primary oligonucleotide or derived oligonucleotide binds is the stretch over which complementarity exists.

At its largest this region is the whole length of the primary oligonucleotide or derived oligonucleotide, but may be shorter if the entire primary sequence or derived oligonucleotide is not complementary to a region of the target sequence. Preferably said part of said region of said target molecule is a stretch of at least 5, e.g. at least 10 or 20 bases, such as from 5 to 100, e.g. 10 to 50 or 15 to 30 bases. This may for example be achieved by said functionally equivalent oligonucleotide having several identical bases to the bases of the primary oligonucleotide or the derived oligonucleotide. These bases may be identical over consecutive stretches, e.g. in apart of the functionally equivalent oligonucleotide, or may be present non-cons ecutively, but provide sufficient complementarity to allow binding to the target sequence. Thus in a prefened feature, said functionally equivalent oligonucleotide hybridizes under conditions of high stringency to a primary oligonucleotide or a derived oligonucleotide or the complementary sequence thereof. Alternatively expressed, said functionally equivalent oligonucleotide exhibits high sequence identity to all or part of a primary oligonucleotide. Preferably said functionally equivalent oligonucleotide has at least 70% sequence identity, preferably at least 80%, e.g. at least 90, 95, 98 or 99%, to all of a primary oligonucleotide or apart thereof. As used in this context, a "part" refers to a stretch of at least 5, e.g. at least 10 or 20 bases, such as from 5 to 100, e.g. 10 to 50 or 15 to 30 bases, in said primary oligonucleotide. Especially preferably when sequence identity to only a part of said primary oligonucleotide is present, the sequence identity is high, e.g. at least 80% as described above. Functionally equivalent oligonucleotides which satisfy the above stated functional requirements include those which are derived from the primary oligonucleotides and also those which have been modified by single or multiple nucleotide base (or equivalent) substitution, addition and/or deletion, but which nonetheless retain functional activity, e.g. bind to the same target molecule as the primary oligonucleotide or the derived oligonucleotide from which they are further derived or modified. Preferably said modification is offrom 1 to 50, e.g. from 10 to 30, preferably from 1 to 5 bases. Especially preferably only minor modifications are present, e.g. variations in less than 10 bases, e.g. less than 5 base changes. Within the meaning of "addition" equivalents are included oligonucleotides containing additional sequences which are complementary to the consecutive stretch of bases on the target molecule to which the primary oligonucleotide or the derived oligonucleotide binds. Alternatively the addition may comprise a different, unrelated sequence, which may for example confer a further property, e.g. to provide a means for immobilization such as a linker to bind the oligonucleotide probe to a solid support. Particularly prefened are naturally occurring equivalents such as biological variants, e.g. allelic, geographical or allotypic variants, e.g. oligonucleotides which conespond to a genetic variant, for example as present in a different species. Functional equivalents include oligonucleotides with modified bases, e.g. using non-naturally occurring bases. Such derivatives may be prepared during synthesis or by post production modification. "Hybridizing" sequences which bind under conditions of low stringency are those which bind under non-stringent conditions (for example, 6x SSC/50% formamide at room temperature) and remain bound when washed under conditions of low stringency (2 X SSC, room temperature, more preferably 2 X SSC, 42°C). Hybridizing under high stringency refers to the above conditions in which washing is performed at 2 X SSC, 65°C (where SSC = 0.15M NaCl, 0.015M sodium citrate, pH 7.2). "Sequence identity" as refened to herein refers to the value obtained when assessed using ClustalW (Thompson et al., 1994, Nucl. Acids Res., 22, p4673-4680) with the following parameters:

Pairwise alignment parameters - Method: accurate, Matrix: IUB, Gap open penalty: 15.00, Gap extension penalty: 6.66; Multiple alignment parameters - Matrix: IUB, Gap open penalty: 15.00, % identity for delay: 30, Negative matrix: no, Gap extension penalty: 6.66, DNA transitions weighting: 0.5. Sequence identity at a particular base is intended to include identical bases which have simply been derivatized. The invention also extends to polypeptides encoded by the mRNA sequence to which a Table 2, 3 or 4 oligonucleotide or a Table 2, 3 or 4 derived oligonucleotide (e.g. having a sequence as defined in Table 2, 5 or 6 or a complementary sequence thereto) binds. The invention further extends to antibodies which bind to any of said polypeptides . As described above, conveniently said set of oligonucleotide probes may be immobilized on one or more solid supports. Single or preferably multiple copies of each unique probe are attached to said solid supports, e.g. 10 or more, e.g. at least 100 copies of each unique probe are present. One or more unique oligonucleotide probes may be associated with separate solid supports which together form a set of probes immobilized on multiple solid support, e.g. one or more unique probes may be immobilized on multiple beads, membranes, filters, biochips etc. which together form a set of probes, which together form modules of the kit described hereinafter. The solid support of the different modules are conveniently physically associated although the signals associated with each probe (generated as described hereinafter) must be separately determinable. Alternatively, the probes may be immobilized on discrete portions of the same solid support, e.g. each unique oligonucleotide probe, e.g. in multiple copies, may be immobilized to a distinct and discrete portion or region of a single filter or membrane, e.g. to generate an anay. A combination of such techniques may also be used, e.g. several solid supports may be used which each immobilize several unique probes. The expression "solid support" shall mean any solid material able to bind oligonucleotides by hydrophobic, ionic or covalent bridges. "Immobilization" as used herein refers to reversible or ineversible association of the probes to said solid support by virtue of such binding. If reversible, the probes remain associated with the solid support for a time sufficient for methods of the invention to be carried out. Numerous solid supports suitable as immobilizing moieties according to the invention, are well known in the art and widely described in the literature and generally speaking, the solid support may be any of the well-known supports or matrices which are cunently widely used or proposed for immobilization, separation etc. in chemical or biochemical procedures. Such materials include, but are not limited to, any synthetic organic polymer such as polystyrene, polyvinylchloride, polyethylene; or nitrocellulose and cellulose acetate; or tosyl activated surfaces; or glass or nylon or any surface carrying a group suited for covalent coupling of nucleic acids. The immobilizing moieties may take the form of particles, sheets, gels, filters, membranes, microfibre strips, tubes or plates, fibres or capillaries, made for example of a polymeric material e.g. agarose, cellulose, alginate, teflon, latex or polystyrene or magnetic beads. Solid supports allowing the presentation of an anay, preferably in a single dimension are prefened, e.g. sheets, filters, membranes, plates or biochips. Attachment of the nucleic acid molecules to the solid support may be performed directly or indirectly. For example if a filter is used, attachment may be performed by UV-induced crosslinking. Alternatively, attachment may be performed indirectly by the use of an attachment moiety carried on the oligonucleotide probes and/or solid support. Thus for example, a pair of affinity binding partners may be used, such as avidin, streptavidin or biotin, DNA or DNA binding protein (e.g. either the lac I repressor protein or the lac operator sequence to which it binds), antibodies (which may be mono- or polyclonal), antibody fragments or the epitopes or haptens of antibodies. In these cases, one partner of the binding pair is attached to (or is inherently part of) the solid support and the other partner is attached to (or is inherently part of) the nucleic acid molecules. As used herein an ''affinity binding pair" refers to two components which recognize and bind to one another specifically (ie. in preference to binding to other molecules). Such binding pairs when bound together form a complex. Attachment of appropriate functional groups to the solid support may be performed by methods well known in the art, which include for example, attachment through hydroxyl, carboxyl, aldehyde or amino groups which may be provided by treating the solid support to provide suitable surface coatings. Solid supports presenting appropriate moieties for attachment of the binding partner may be produced by routine methods known in the art. Attachment of appropriate functional groups to the oligonucleotide probes of the invention may be performed by ligation or introduced during synthesis or amplification, for example using primers carrying an appropriate moiety, such as biotin or a particular sequence for capture. Conveniently, the set of probes described hereinbefore is provided in kit form. Thus viewed from a further aspect the present invention provides a kit comprising a set of oligonucleotide probes as described hereinbefore immobilized on one or more solid supports. Preferably, said probes are immobilized on a single solid support and each unique probe is attached to a different region of said solid support. However, when attached to multiple solid supports, said multiple solid supports form the modules which make up the kit. Especially preferably said solid support is a sheet, filter, membrane, plate or biochip. Optionally the kit may also contain information relating to the signals generated by normal or diseased samples (as discussed in more detail hereinafter in relation to the use of the kits), standardizing materials, e.g. mRNA or cDNA from normal and/or diseased samples for comparative puφoses, labels for incoφoration into cDNA, adapters for introducing nucleic acid sequences for amplification puφoses, primers for amplification and/or appropriate enzymes, buffers and solutions. Optionally said kit may also contain a package insert describing how the method of the invention should be performed, optionally providing standard graphs, data or software for inteφretation of results obtained when performing the invention. The use of such kits to prepare a standard diagnostic gene transcript pattern as described hereinafter forms a further aspect of the invention. The set of probes as described herein have various uses. Principally however they are used to assess the gene expression state of a test cell to provide information relating to the organism from which said cell is derived. Thus the probes are useful in diagnosing, identifying or monitoring a cancer or stage thereof in an organism. Thus in a further aspect the invention provides the use of a set of oligonucleotide probes or a kit as described hereinbefore to determine the gene expression pattern of a cell which pattern reflects the level of gene expression of genes to which said oligonucleotide probes bind, comprising at least the steps of: a) isolating mRNA from said cell, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of ol igonucleotide probes or a kit as defined herein; and c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce said pattern. The mRNA and cDNA as refened to in this method, and the methods hereinafter, encompass derivatives or copies of said molecules, e.g. copies of such molecules such as those produced by amplification or the preparation of complementary strands, but which retain the identity of the mRNA sequence, ie. would hybridize to the direct transcript (or its complementary sequence) by virtue of precise complementarity, or sequence identity, over at least a region of said molecule. It will be appreciated that complementarity will not exist over the entire region where techniques have been used which may truncate the transcript or introduce new sequences, e.g. by primer amplification. For convenience, said mRNA or cDNA is preferably amplified prior to step b). As with the oligonucleotides described herein said molecules may be modified, e.g. by using non-natural bases during synthesis providing complementarity remains. Such molecules may also carry additional moieties such as signalling or immobilizing means. The various steps involved in the method of preparing such a pattern are described in more detail hereinafter. As used herein "gene expression" refers to transcription of a particular gene to produce a specific mRNA product (ie. a particular splicing product). The level of gene expression may be determined by assessing the level of transcribed mRNA molecules or cDNA molecules reverse transcribed from the mRNA molecules or products derived from those molecules, e.g. by amplification. The "pattern" created by this technique refers to information which, for example, may be represented in tabular or graphical form and conveys information about the signal associated with two or more oligonucleotides. Preferably said pattern is expressed as an anay of numbers relating to the expression level associated with each probe. Preferably, said pattern is established using the following linear model: y = Xb + f Equation 1 wherein, X is the matrix of gene expression data and y is the response variable, b is the regression coefficient vector and f the estimated residual vector. Although many different methods can be used to establish the relationship provided in equation 1, especially preferably the partial Least Squares Regression (PLSR) method is used for establishing the relationship in equation 1. The probes are thus used to generate a pattern which reflects the gene expression of a cell at the time of its isolation. The pattern of expression is characteristic of the circumstances under which that cells finds itself and depends on the influences to which the cell has been exposed. Thus, a characteristic gene transcript pattern standard or fingeφrint (standard probe pattern) for cells from an individual with a particular cancer may be prepared and used for comparison to transcript patterns of test cells. This has clear applications in diagnosing, monitoring or identifying whether an organism is suffering from a particular cancer or stage thereof. The standard pattern is prepared by determining the extent of binding of total mRNA (or cDNA or related product), from cells from a sample of one or more organisms with the cancer or stage thereof, to the probes. This reflects the level of transcripts which are present which conespond to each unique probe. The amount of nucleic acid material which binds to the different probes is assessed and this information together forms the gene transcript pattern standard of that cancer or stage thereof. Each such standard pattern is characteristic of the cancer or stage thereof. In a further aspect therefore, the present invention provides a method of preparing a standard gene transcript pattern characteristic of a cancer or stage thereof in an organism comprising at least the steps of: a) isolating mRNA from the cells of a sample of one or more organisms having the cancer or stage thereof, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotides or a kit as described hereinbefore specific for said cancer or stage thereof in an organism and sample thereof corresponding to the organism and sample thereof under investigation; and c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce a characteristic pattern reflecting the level of gene expression of genes to which said oligonucleotides bind, in the sample with the cancer or stage thereof. For convenience, said oligonucleotides are preferably immobilized on one or more solid supports. The standard pattern for a great number of cancers and different stages thereof using particular probes may be accumulated in databases and be made available to laboratories on request. "Disease" samples and organisms or "cancer" samples and organisms as referred to herein refer to organisms (or samples from the same) with abnormal cell proliferation e.g. in a solid mass such as a tumour. Such organisms are known to have, or which exhibit, the cancer or stage thereof under study. "Stages" thereof refer to different stages of the cancer which may or may not exhibit particular physiological or metabolic changes, but do exhibit changes at the genetic level which may be detected as altered gene expression. It will be appreciated that during the course of a cancer the expression of different transcripts may vary. Thus at different stages, altered expression may not be exhibited for particular transcripts compared to "normal" samples. However, combining information from several transcripts which exhibit altered expression at one or more stages through the course of the cancer can be used to provide a characteristic pattern which is indicative of a particular stage of the cancer. Thus for example different stages in cancer, e.g. pre-stage I, stage I, stage II, II or TV can be identified. "Normal" as used herein refers to organisms or samples which are used for comparative puφoses. Preferably, these are "normal" in the sense that they do not exhibit any indication of, or are not believed to have, any disease or condition that would affect gene expression, particularly in respect of cancer for which they are to be used as the normal standard. However, it will be appreciated that different stages of a cancer may be compared and in such cases, the "normal" sample may conespond to the earlier stage of the cancer. As used herein a "sample" refers to any material obtained from the organism, e.g. human or non-human animal under investigation which contains cells and includes, tissues, body fluid or body waste or in the case of prokaryotic organisms, the organism itself. "Body fluids" include blood, saliva, spinal fluid, semen, lymph. "Body waste" includes urine, expectorated matter (pulmonary patients), faeces etc. "Tissue samples" include tissue obtained by biopsy, by surgical interventions or by other means e.g. placenta. Preferably however, the samples which are examined are from areas of the body not apparently affected by the cancer. The cells in such samples are not disease cells, i.e. cancer cells, have not been in contact with such disease cells and do not originate from the site of the cancer. The "site of disease" is considered to be that area of the body which manifests the disease in away which may be objectively determined, e.g. a tumour. Thus for example peripheral blood may be used for the diagnosis of non-haematopoietic cancers, and the blood does not require the presence of malignant or disseminated cells from the cancer in the blood. Similarly in diseases of the brain, in which no diseased cells are found in the blood due to the blood: brain barrier, peripheral blood may still be used in the methods of the invention. It will however be appreciated that the method of preparing the standard transcription pattern and other methods of the invention are also applicable for use on living parts of eukaryotic organisms such as cell lines and organ cultures and explants. As used herein, reference to "conesponding" sample etc. refers to cells preferably from the same tissue, body fluid or body waste, but also includes cells from tissue, body fluid or body waste which are sufficiently similar for the puφoses of preparing the standard or test pattern. When used in reference to genes "conesponding" to the probes, this refers to genes which are related by sequence (which may be complementary) to the probes although the probes may reflect different splicing products of expression. "Assessing" as used herein refers to both quantitative and qualitative assessment which may be determined in absolute or relative terms. The invention may be put into practice as follows. To prepare a standard transcript pattern for a particular cancer or stage thereof, sample mRNA is extracted from the cells of tissues, body fluid or body waste according to known techniques (see for example Sambrook et. al. (1989), Molecular Cloning : A laboratory manual, 2nd Ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NN.) from a diseased individual or organism. Owing to the difficulties in working with RΝA, the RΝA is preferably reverse transcribed at this stage to form first strand cDΝA. Cloning of the cDΝA or selection from, or using, a cDΝA library is not however necessary in this or other methods of the invention. Preferably, the complementary strands of the first strand cDΝAs are synthesized, ie. second strand cDΝAs, but this will depend on which relative strands are present in the oligonucleotide probes. The RΝA may however alternatively be used directly without reverse transcription and may be labelled if so required. Preferably the cDΝA strands are amplified by known amplification techniques such as the polymerase chain reaction (PCR) by the use of appropriate primers. Alternatively, the cDΝA strands may be cloned with a vector, used to transform a bacteria such as E. coli which may then be grown to multiply the nucleic acid molecules. When the sequence of the cDΝAs are not known, primers may be directed to regions of the nucleic acid molecules which have been introduced. Thus for example, adapters may be ligated to the cDNA molecules and primers directed to these portions for amplification of the cDNA molecules. Alternatively, in the case of eukaryotic samples, advantage may be taken of the polyA tail and cap of the RNA to prepare appropriate primers. To produce the standard diagnostic gene transcript pattern or fϊngeφrint for a particular cancer or stage thereof, the above described oligonucleotide probes are used to probe mRNA or cDNA of the diseased sample to produce a signal for hybridization to each particular oligonucleotide probe species, ie. each unique probe. A standard control gene transcript pattern may also be prepared i f desired using mRNA or cDNA from a normal sample. Thus, mRNA or cDNA is brought into contact with the oligonucleotide probe under appropriate conditions to allow hybridization. When multiple samples are probed, this may be performed consecutively using the same probes, e.g. on one or more solid supports, ie. on probe kit modules, or by simultaneously hybridizing to corresponding probes, e.g. the modules of a conesponding probe kit. To identify when hybridization occurs and obtain an indication of the number of transcripts/cDNA molecules which become bound to the oligonucleotide probes, it is necessary to identify a signal produced when the transcripts (or related molecules) hybridize (e.g. by detection of double stranded nucleic acid molecules or detection of the number of molecules which become bound, after removing unbound molecules, e.g. by washing). In order to achieve a signal, either or both components which hybridize (ie. the probe and the transcript) carry or form a signalling means or a part thereof. This "signalling means" is any moiety capable of direct or indirect detection by the generation or presence of a signal. The signal may be any detectable physical characteristic such as confened by radiation emission, scattering or absoφtion properties, magnetic properties, or other physical properties such as charge, size or binding properties of existing molecules (e.g. labels) or molecules which may be generated (e.g. gas emission etc.).

Techniques are prefened which allow signal amplification, e.g. which produce multiple signal events from a single active binding site, e.g. by the catalytic action of enzymes to produce multiple detectable products. Conveniently the signalling means may be a label which itself provides a detectable signal. Conveniently this may be achieved by the use of a radioactive or other label which may be incoφorated during cDNA production, the preparation of complementary cDNA strands, during amplification of the target m-RNA/cDNA or added directly to target nucleic acid molecules. Appropriate labels are those which directly or indirectly allow detection or measurement of the presence of the transcripts/cDNA. Such labels include for example radiolabels, chemical labels, for example chromophores or fluorophores (e.g. dyes such as fluorescein and rhodamine), or reagents of high electron density such as fenitin, haemocyanin or colloidal gold. Alternatively, the label may be an enzyme, for example peroxidase or alkaline phosphatase, wherein the presence of the enzyme is visualized by its interaction with a suitable entity, for example a substrate. The label may also form part of a signalling pair wherein the other member of the pair is found on, or in close proximity to, the oligonucleotide probe to which the transcript/cDNA binds, for example, a fluorescent compound and a quench fluorescent substrate may be used. A label may also be provided on a different entity, such as an antibody, which recognizes a peptide moiety attached to the transcripts/cDNA, for example attached to a base used during synthesis or amplification. A signal may be achieved by the introduction of a label before, during or after the hybridization step. Alternatively, the presence of hybridizing transcripts may be identified by other physical properties, such as their absorbance, and in which case the signalling means is the complex itself. The amount of signal associated with each oligonucleotide probe is then assessed. The assessment may be quantitative or qualitative and may be based on binding of a single transcript species (or related cDNA or other products) to each probe, or binding of multiple transcript species to multiple copies of each unique probe. It will be appreciated that quantitative results will provide further information for the transcript fϊngeφrint of the cancer which is compiled. This data may be expressed as absolute values (in the case of macroanays) or may be determined relative to a particular standard or reference e.g. a normal control sample. Furthermore it will be appreciated that the standard diagnostic gene pattern transcript may be prepared using one or more disease samples (and normal samples if used) to perform the hybridization step to obtain patterns not biased towards a particular individual's variations in gene expression. The use of the probes to prepare standard patterns and the standard diagnostic gene transcript patterns thus produced for the puφose of identification or diagnosis or monitoring of a particular cancer or stage thereof in a particular organism forms a further aspect of the invention. Once a standard diagnostic fingeφrint or pattern has been determined for a particular cancer or stage thereof using the selected oligonucleotide probes, this information can be used to identify the presence, absence or extent or stage of that cancer in a different test organism or individual. To examine the gene expression pattern of a test sample, a test sample of tissue, body fluid or body waste containing cells, conesponding to the sample used for the preparation of the standard pattern, is obtained from a patient or the organism to be studied. A test gene transcript pattern is then prepared as described hereinbefore as for the standard pattern. In a further aspect therefore, the present invention provides a method of preparing a test gene transcript pattern comprising at least the steps of: a) isolating mRNA from the cells of a sample of said test organism, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotides or a kit as described hereinbefore specific for a cancer or stage thereof in an organism and sample thereof conesponding to the organism and sample thereof under investigation; and c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce said pattem reflecting the level of gene expression of genes to which said oligonucleotides bind, in said test sample. This test pattem may then be compared to one or more standard patterns to assess whether the sample contains cells having the cancer or stage thereof. Thus viewed from a further aspect the present invention provides a method of diagnosing or identifying or monitoring a cancer or stage thereof in an organism, comprising the steps of: a) isolating mRNA from the cells of a sample of said organism, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotides or a kit as described hereinbefore specific for said cancer or stage thereof in an organism and sample thereof conesponding to the organism and sample thereof under investigation; c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce a characteristic pattern reflecting the level of gene expression of genes to which said oligonucleotides bind, in said sample; and d) comparing said pattern to a standard diagnostic pattern prepared according to the method of the invention using a sample from an organism conesponding to the organism and sample under investigation to determine the presence of said cancer or a stage thereof in the organism under investigation. The method up to and including step c) is the preparation of a test pattern as described above. As refened to herein, "diagnosis" refers to determination of the presence or existence of a cancer or stage thereof in an organism. "Monitoring" refers to establishing the extent of a cancer, particularly when an individual is known to be suffering from cancer, for example to monitor the effects of treatment or the development of a cancer, e.g. to determine the suitability of a treatment or provide a prognosis. The presence of the cancer or stage thereof may be determined by determining the degree of conelation between the standard and test samples' patterns. This necessarily takes into account the range of values which are obtained for normal and diseased samples. Although this can be established by obtaining standard deviations for several representative samples binding to the probes to develop the standard, it will be appreciated that single samples may be sufficient to generate the standard pattern to identify a cancer if the test sample exhibits close enough conelation to that standard. Conveniently, the presence, absence, or extent of a cancer or stage thereof in a test sample can be predicted by inserting the data relating to the expression level of informative probes in test sample into the standard diagnostic probe pattern established according to equation 1. Data generated using the above mentioned methods may be analysed using various techniques from the most basic visual representation (e.g. relating, to intensity) to more complex data manipulation to identify underlying patterns which reflect the intenelationship of the level of expression of each gene to which the various probes bind, which may be quantified and expressed mathematically. Conveniently, the raw data thus generated may be manipulated by the data processing and statistical methods described hereinafter, particularly normalizing and standardizing the data and fitting the data to a classification model to determine whether said test data reflects the pattern of a particular cancer or stage thereof. The methods described herein may be used to identify, monitor or diagnose a cancer or its stage or progression, for which the oligonucleotide probes are informative. "Informative" probes as described herein, are those which reflect genes which have altered expression in the cancer in question, or particular stages thereof. Probes of the invention may not be sufficiently informative for diagnostic puφoses when used alone, but are informative when used as one of several probes to provide a characteristic pattern, e.g. in a set as described hereinbefore. Preferably said probes conespond to genes which are systemically affected by said cancer or stage thereof. Especially preferably said genes, from which transcripts are derived which bind to probes of the invention, are moderately or highly expressed. The advantage of using probes directed to moderately or highly expressed genes is that smaller clinical samples are required for generating the necessary gene expression data set, e.g. less than 1ml blood samples. Furthermore, it has been found that such genes which are already being actively transcribed tend to be more prone to being influenced, in a positive or negative way, by new stimuli. In addition, since transcripts are already being produced at levels which are generally detectable, small changes in those levels are readily detectable as for example, a certain detectable threshold does not need to be reached. In preferred methods of the invention, the set of probes of the invention are informative for a variety of different cancers or stages thereof. A sub-set of the probes disclosed herein may be used for diagnosis, identification or monitoring a particular cancer or stage thereof. Cancers for which the probes may be used for diagnosis, identification and monitoring include stomach, lung, breast, prostate gland, bowel, skin, colon and ovary cancer. Especially preferably the probes are used for breast cancer analysis. The diagnostic method may be used alone as an alternative to other diagnostic techniques or in addition to such techniques. For example, methods of the invention may be used as an alternative or additive diagnostic measure to diagnosis using imaging techniques such as Magnetic Resonance Imagine

(MRI), ultrasound imaging, nuclear imaging or X-ray imaging, for example in the identification and/or diagnosis of tumours. The methods of the invention may be performed on cells from prokaryotic or eukaryotic organisms which may be any eukaryotic organisms such as human beings, other mammals and animals, birds, insects, fish and plants, and any prokaryotic organism such as a bacteria. Prefened non-human animals on which the methods of the invention may be conducted include, but are not limited to mammals, particularly primates, domestic animals, livestock and laboratory animals. Thus prefened animals for diagnosis include mice, rats, guinea pigs, cats, dogs, pigs, cows, goats, sheep, horses. Particularly preferably cancer of humans is diagnosed, identified or monitored. As described above, the sample under study may be any convenient sample which may be obtained from an organism. Preferably however, as mentioned above, the sample is obtained from a site distant to the site of disease and the cells in such samples are not disease cells, have not been in contact with such cells and do not originate from the site of the disease. In such cases, although preferably absent, the sample may contain cells which do not fulfil these criteria. However, since the probes of the invention are concerned with transcripts whose expression is altered in cells which do satisfy these criteria, the probes are specifically directed to detecting changes in transcript levels in those cells even if in the presence of other, background cells. It has been found that the cells from such samples show significant and informative variations in the gene expression of a large number of genes. Thus, the same probe (or several probes) may be found to be informative in determinations regarding two or more cancers, or stages thereof by virtue of the particular level of transcripts binding to that probe or the intenelationship of the extent of binding to that probe relative to other probes. As a consequence, it is possible to use a relatively small number of probes for screening for multiple cancers. This has consequences with regard to the selection of probes, but also for the use of a single set of probes for more than one diagnosis. Thus, the present invention also provides sets of probes for diagnosing, identifying or monitoring two or more cancers or stages thereof, wherein at least one of said probes is suitable for said diagnosing, identifying or monitoring at least two of said cancers or stages thereof, and kits and methods of using the same. Preferably at least 5 probes, e.g. from 5 to 15 probes, are used in at least two diagnoses. Thus, in a further prefened aspect, the present invention provides a method of diagnosis or identification or monitoring as described hereinbefore for the diagnosis, identification or monitoring of two or more cancers or stages thereof in an organism, wherein said test pattern produced in step c) of the diagnostic method is compared in step d) to at least two standard diagnostic patterns prepared as described previously, wherein each standard diagnostic pattern is a pattem generated for a different cancer or stage thereof. Whilst in a prefened aspect the methods of assessment concern the development of a gene transcript pattern from a test sample and comparison of the same to a standard pattern, the elevation or depression of expression of certain markers may also be examined by examining the products of expression and the level of those products. Thus a standard pattern in relation to the expressed product may be generated. In such methods the levels of expression of a set of polypeptides encoded by the gene to which a primary oligonucleotide or a derived oligonucleotide, binds, are analysed. Various diagnostic methods may be used to assess the amount of polypeptides (or fragments thereof) which are present. The presence or concentration of polypeptides may be examined, for example by the use of a binding partner to said polypeptide (e.g. an antibody), which may be immobilized, to separate said polypeptide from the sample and the amount of polypeptide may then be determined. "Fragments" of the polypeptides refers to a domain or region of said polypeptide, e.g. an antigenic fragment, which is recognizable as being derived from said polypeptide to allow binding of a specific binding partner. Preferably such a fragment comprises a significant portion of said polypeptide and corcesponds to a product of normal post-synthesis processing. Thus in a further aspect the present invention provides a method of preparing a standard gene transcript pattern characteristic of a cancer or stage thereof in an organism comprising at least the steps of: a) releasing target polypeptides from a sample of one or more organisms having the cancer or stage thereof; b) contacting said target polypeptides with one or more binding partners, wherein each binding partner is specific to a marker polypeptide (or a fragment thereof) encoded by the gene to which a primary oligonucleotide (or derived sequence) binds, to allow binding of said binding partners to said target polypeptides, wherein said marker polypeptides are specific for said cancer in an organism and sample thereof conesponding to the organism and sample thereof under investigation; and c) assessing the target polypeptide binding to said binding partners to produce a characteristic pattern reflecting the level of gene expression of genes which express said marker polypeptides, in the sample with the cancer or stage thereof. As used herein "target polypeptides" refer to those polypeptides present in a sample which are to be detected and "marker polypeptides" are polypeptides which are encoded by the genes to which primary oligonucleotides or derived oligonucleotides bind, ie. genes within the gene families. The target and marker polypeptides are identical or at least have areas of high similarity, e.g. epitopic regions to allow recognition and binding of the binding partner. "Release" of the target polypeptides refers to appropriate treatment of a sample to provide the polypeptides in a form accessible for binding of the binding partners, e.g. by lysis of cells where these are present. The samples used in this case need not necessarily comprise cells as the target polypeptides may be released from cells into the sunounding tissue or fluid, and this tissue or fluid may be analysed, e.g. urine or blood. Preferably however the prefened samples as described herein are used. "Binding partners" comprise the separate entities which together make an affinity binding pair as described above, wherein one partner of the binding pair is the target or marker polypeptide and the other partner binds specifically to that polypeptide, e.g. an antibody. Various anangements may be envisaged for detecting the amount of binding pairs which form. In its simplest form, a sandwich type assay e.g. an immunoassay such as an ELISA, may be used in which an antibody specific to the polypeptide and carrying a label (as described elsewhere herein) may be bound to the binding pair (e.g. the first antibody:polypeptide pair) and the amount of label detected. Other methods as described herein may be similarly modified for analysis of the protein product of expression rather than the gene transcript and related nucleic acid molecules . Thus a further aspect of the invention provides a method of preparing a test gene transcript pattern comprising at least the steps of: a) releasing target polypeptides from a sample of said test organism; b) contacting said target polypeptides with one or more binding partners, wherein each binding partner is specific to a marker polypeptide (or a fragment thereof) encoded by the gene to which a primary oligonucleotide (or derived sequence) binds, to allow binding of said binding partners to said target polypeptides, wherein said marker polypeptides are specific for said cancer in an organism and sample thereof conesponding to the organism and sample thereof under investigation; and c) assessing the target polypeptide binding to said binding partners to produce a characteristic pattern reflecting the level of gene expression of genes which express said marker polypeptides, in said test sample. A yet further aspect of the invention provides a method of diagnosing or identifying or monitoring a cancer or stage thereof in an organism comprising the steps of: a) releasing target polypeptides from a sample of said organism; b) contacting said target polypeptides with one or more binding partners, wherein each binding partner is specific to a marker polypeptide (or a fragment thereof) encoded by the gene to which a primary oligonucleotide (or derived sequence) binds, to allow binding of said binding partners to said target polypeptides, wherein said marker polypeptides are specific for said cancer in an organism and sample thereof conesponding to the organism and sample thereof under investigation; and c) assessing the target polypeptide binding to said binding partners to produce a characteristic pattern reflecting the level of gene expression of genes which express said marker polypeptides in said sample; and d) comparing said pattern to a standard diagnostic pattern prepared as described hereinbefore using a sample from an organism conesponding to the organism and sample under investigation to determine the degree of correlation indicative of the presence of said cancer or a stage thereof in the organism unde r investigation. The methods of generating standard and test patterns and diagnostic techniques rely on the use of informative oligonucleotide probes to generate the gene expression data. In some cases it will be necessary to select these informative probes for a particular method, e.g. to diagnose a particular cancer, from a selection of available probes, e.g. the Table 2 and/or 3 oligonucleotides, the Table 2 and or 3 derived oligonucleotides, their complementary sequences and functionally equivalent oligonucleotides and optionally the Table 4 oligonucleotides, their derived oligonucleotides, complementary sequences and functionally equivalent oligonucleotides. Said derived oligonucleotides include oligonucleotides derived from the genes conesponding to the sequences provided in those tables, e.g. the genes set forth in Tables 2, 5 or 6 (see the Accession numbers), or the complementary sequences thereof. The following methodology describes a convenient method for identifying such informative probes, or more particularly how to select a suitable sub-set of probes from the probes described herein. Probes for the analysis of a particular cancer or stage thereof, may be identified in a number of ways known in the prior art, including by differential expression or by library subtraction (see for example WO98/49342). As described in PCT/GB03/005102 and as described hereinafter, in view of the high information content of most transcripts, as a starting point one may also simply analyse a random sub-set of mRNA or cDNA species corresponding to the family of sequence described herein and pick the most informative probes from that sub-set. The following method describes the use of immobilized oligonucleotide probes (e.g. the probes of the invention) to which mRNA (or related molecules) from different samples are bound to identify which probes are the most informative to identify a particular type of cancer, e.g. a disease sample. The immobilized probes can be derived from various unrelated or related organisms; the only requirement is that the immobilized probes should bind specifically to their homologous counteφarts in test organisms. Probes can also be derived from commercially available or public databases and immobilized on solid supports. The selected probes necessarily conespond to one of the genes in the gene sequence families described herein, but the probes of interest may be randomly selected from within that entire group of families. The length of the probes immobilised on the solid support should be long enough to allow for specific binding to the target sequences. The immobilised probes can be in the form of DNA, RNA or their modified products or PNAs

(peptide nucleic acids). Preferably, the probes immobilised should bind specifically to their homologous counteφarts representing highly and moderately expressed genes in test organisms. Conveniently the probes which are used are the probes described herein. The gene expression pattern of cells in biological samples can be generated using prior art techniques such as microarray or macroarray as described below or using methods described herein. Several technologies have now been developed for monitoring the expression level of a large number of . genes simultaneously in biological samples, such as, high-density oligoanays (Lockhart et al., 1996, Nat. Biotech., 14, pl675-1680), cDNA microarrays (Schena et al, 1995, Science, 270, p467-470) and cDNA macroanays (Maier E et al., 1994, Nucl. Acids Res., 22, p3423-3424; Bernard et al., 1996, Nucl. Acids

Res., 24, pl435-1442). In high-density oligoanays and cDNA microarrays, hundreds and thousands of probe oligonucleotides or cDNAs, are spotted onto glass slides or nylon membranes, or synthesized on biochips. The mRNA isolated from the test and reference samples are labelled by reverse transcription with a red or green fluorescent dye, mixed, and hybridised to the microarray. After washing, the bound fluorescent dyes are detected by a laser, producing two images, one for each dye. The resulting ratio of the red and green spots on the two images provides the information about the changes in expression levels of genes in the test and reference samples. Alternatively, single channel or multiple channel microarray studies can also be performed. In cDNA macroarray, different cDNAs are spotted on a solid support such as nylon membranes in excess in relation to the amount of test mRNA that can hybridise to each spot. mRNA isolated from test samples is radio- labelled by reverse transcription and hybridised to the immobilised probe cDNA. After washing, the signals associated with labels hybridising specifically to immobilised probe cDNA are detected and quantified. The data obtained in macroanay contains information about the relative levels of transcripts present in the test samples. Whilst macroanays are only suitable to monitor the expression of a limited number of genes, microarrays can be used to monitor the expression of several thousand genes simultaneously and is, therefore, a prefened choice for large-scale gene expression studies. A macroarray technique for generating the gene expression data set has been used to illustrate the probe identification method described herein. For this puφose, mRNA is isolated from samples of interest and used to prepare labelled target molecules, e.g. mRNA or cDNA as described above. The labelled target molecules are then hybridised to probes immobilised on the solid support.

Various solid supports can be used for the puφose, as described previously. Following hybridization, unbound target molecules are removed and signals from target molecules hybridizing to immobilised probes quantified. If radio labelling is performed, Phosphohnager can be used to generate an image file that can be used to generate a raw data set. Depending on the nature of label chosen for labelling the target molecules, other instruments can also be used, for example, when fluorescence is used for labelling, a Fluorolmager can be used to generate an image file from the hybridised target molecules. The raw data conesponding to mean intensity, median intensity, or volume of the signals in each spot can be acquired from the image file using commercially available software for image analysis. However, the acquired data needs to be corrected for background signals and normalized prior to analysis, since, several factors can affect the quality and quantity of the hybridising signals. For example, variations in the quality and quantity of mRNA isolated from sample to sample, subtle variations in the efficiency of labelling target molecules during each reaction, and variations in the amount of unspecific binding between different macroanays can all contribute to noise in the acquired data set that must be corrected for prior to analysis. Background conection can be performed in several ways. The lowest pixel intensity within a spot can be used for background subtraction or the mean or median of the line of pixels around the spots' outline can be used for the puφose. One can also define an area representing the background intensity based on the signals generated from negative controls and use the average intensity of this area for background subtraction. The background conected data can then be transformed for stabilizing the variance in the data structure and normalized for the differences in probe intensity. Several transformation techniques have been described in the literature and a brief overview can be found in Cui, err and Churchill htφ://www.jax.org/research/ churchill/research exρression/Cui-Transform.pdf). Normalization can be performed by dividing the intensity of each spot with the collective intensity, average intensity or median intensity of all the spots in a macroarray or a group of spots in a macroanay in order to obtain the relative intensity of signals hybridising to immobilised probes in a macroarray. Several- methods have been described for normalizing gene expression data (Richmond and Somerville, 2000, Cunent Opin. Plant Biol., 3, pl08-l 16; Finkelstein et al., 2001, In "Methods of Microarray Data Analysis. Papers from CAMDA, Eds. Lin & ohnsom, Kluwer Academic, p57-68; Yang et al., 2001, In "Optical

Technologies and Informatics", Eds. Bittner, Chen, Dorsel & Dougherty, Proceedings of SPIE, 4266, pl41-152; Dudoit et al, 2000, J. Am. Stat. Ass., 97, p77-87; Alter et al 2000, supra; Newton et al., 2001, J. Comp. Biol., 8, p37-52). Generally, a scaling factor or function is first calculated to conect the intensity effect and then used for normalising the intensities. The use of external controls has also been suggested for improved normalization. One other major challenge encountered in large-scale gene expression analysis is that of standardization of data collected from experiments performed at different times. We have observed that gene expression data for samples acquired in the same experiment can be efficiently compared following background correction and normalization. However, the data from samples acquired in experiments performed at different times requires further standardization prior to analysis. This is because subtle differences in experimental parameters between different experiments, for example, differences in the quality and quantity of mRNA extracted at different times, differences in time used for target molecule labelling, hybridization time or exposure time, can affect the measured values. Also, factors such as the nature of the sequence of transcripts under investigation (their GC content) and their amount in relation to the each other determines how they are affected by subtle variations in the experimental processes. They determine, for example, how efficiently first strand cDNAs, conesponding to a particular transcript, are transcribed and labelled during first strand synthesis, or how efficiently the conesponding labelled target molecules bind to their complementary sequences during hybridization. Batch to batch difference in the printing process is also a major factor for variation in the generated expression data. Failure to properly address and rectify for these influences leads to situations where the differences between the experimental series may overshadow the main information of interest contained in the gene expression data set, i.e. the differences within the combined data from the different experimental series. Hence, when required the expression data should be batch- adjusted prior to data analysis. Monitoring the expression of a large number of genes in several samples leads to the generation of a large amount of data that is too complex to be easily inteφreted. Several unsupervised and supervised multivariate data analysis techniques have already been shown to be useful in extracting meaningful biological information from these large data sets. Cluster analysis is by far the most commonly used technique for gene expression analysis, and has been performed to identify genes that are regulated in a similar manner, and or identifying new/unknown tumour classes using gene expression profiles (Eisen et al., 1998, PNAS, 95, pl4863-14868, Alizadeh et al. 2000, supra, Perou et al. 2000, Nature, 406, p747-752; Ross et al, 2000, Nature Genetics, 24(3), p227- 235; Herwig et al., 1999, Genome Res., 9, pl093-l 105; Tamayo et al, 1999,

Science, PNAS, 96, p2907-2912). In the clustering method, genes are grouped into functional categories (clusters) based on their expression profile, satisfying two criteria: homogeneity - the genes in the same cluster are highly similar in expression to each other; and separation - genes in different clusters have low similarity in expression to each other. Examples of various clustering techniques that have been used for gene expression analysis include hierarchical clustering (Eisen et al., 1998, supra; Alizadeh et al. 2000, supra; Perou et al. 2000, supra; Ross et al, 2000, supra), K-means clustering (Herwig et al., 1999, supra; Tavazoie et al, 1999, Nature

Genetics, 22(3), p. 281-285), gene shaving (Hastie et al., 2000, Genome Biology, 1(2), research 0003.1-0003.21), block clustering (Tibshirani et al., 1999, Tech report Univ Stanford.) Plaid model (Lazzeroni, 2002, Stat. Sinica, 12, p61-86), and self-organizing maps (Tamayo et al. 1999, supra). Also, related methods of multivariate statistical analysis, such as those using the singular value decomposition (Alter et al., 2000, PNAS, 97(18), ρlOlOl-10106; Ross et al. 2000, supra) or multidimensional scaling can be effective at reducing the dimensions of the objects under study. However, methods such as cluster analysis and singular value decomposition are purely exploratory and only provide abroad overview of the internal structure present in the data. They are unsupervised approaches in which the available information concerning the nature of the class under investigation is not used in the analysis. Often, the nature of the biological perturbation to which a particular sample has been subjected is known. For example, it is sometimes known whether the sample whose gene expression pattern is being analysed derives from a diseased or healthy individual. In such instances, discriminant analysis can be used for classifying samples into various groups based on their gene expression data. In such an analysis one builds the classifier by training the data that is capable of discriminating between member and non-members of a given class. The trained classifier can then be used to predict the class of unknown samples. Examples of discrimination methods that have been described in the literature include Support Vector Machines (Brown et al, 2000, PNAS, 97, p262-267), Nearest Neighbour (Dudoit et al., 2000, supra), Classification trees (Dudoit et al., 2000, supra), Voted classification (Dudoit et al., 2000, supra), Weighted Gene voting (Golub et al. 1999, supra), and Bayesian classification (Keller et al. 2000, Tec report Univ of Washington). Also a technique in which PLS (Partial Least Square) regression analysis is first used to reduce the dimensions in the gene expression data set followed by classification using logistic discriminant analysis and quadratic discriminant analysis (LD and QDA) has recently been described (Nguyen & Rocke, 2002, Bioinformatics, 18, p39-50 and 1216-1226). A challenge that gene expression data poses to classical discriminatory methods is that the number of genes whose expression are being analysed is very large compared to the number of samples being analysed. However in most cases only a small fraction of these genes are informative in discriminant analysis problems. Moreover, there is a danger that the noise from irrelevant genes can mask or distort the information from the informative genes. Several methods have been suggested in literature to identify and select genes that are informative in microanay studies, for example, t-statistics (Dudoit et al, 2002, J.

Am. Stat. Ass., 97, p77-87), analysis of variance (Kerr et al., 2000, PNAS, 98, p8961-8965), Neighbourhood analysis (Golub et al, 1999, supra), Ratio of between groups to within groups sum of squares (Dudoit et al., 2002, supra), Non parametric scoring (Park et al., 2002, Pacific Symposium on Biocomputing, p52-63) and Likelihood selection (Keller et al., 2000, supra). In the methods described herein the gene expression data that has been normalized and standardized is analysed by using Partial Least Squares Regression (PLSR). Although PLSR is primarily a method used for regression analysis of continuous data (see Appendix A), it can also be utilized as a method for model building and discriminant analysis using a dummy response matrix based on a binary coding. The class assignment is based on a simple dichotomous distinction such as breast cancer (class 1) / healthy (class 2), or a multiple distinction based on multiple disease diagnosis such as breast cancer (class 1) / ovarian cancer (class 2) /healthy (class 3). The list of diseases for classification can be increased depending upon the samples available conesponding to other cancers or stages thereof. PLSR applied as a classification method is referred to as PLS-DA (DA standing for Discriminant analysis). PLS-DA is an extension of the PLSR algorithm in which the Y-matrix is a dummy matrix containing n rows (conesponding to the number of samples) and K columns (conesponding to the number of classes). The Y-matrix is constructed by inserting 1 in the kt column and -1 in all the other columns if the conesponding zth object of X belongs to class k. By regressing Y onto X, classification of a new sample is achieved by selecting the group conesponding to the largest component of the fitted, )(x) = ( ι(x), ⁵2(x)₃--_> (^χ))- Thus, in a-1/1 response matrix, a prediction value below 0 means that the sample belongs to the class designated as -1, while a prediction value above 0 implies that the sample belongs to the class designated as 1. An advantage of PLSR-DA is that the results obtained can be easily represented in the form of two different plots, the score and loading plots. Score plots represent a projection of the samples onto the principal components and shows the distribution of the samples in the classification model and their relationship to one another. Loading plots display conelations between the variables present in the data set. It is usually recommended to use PLS-DA as a starting point for the classification problem due to its ability to handle collinear data, and the property of PLSR as a dimension reduction technique. Once this puφose has been satisfied, it is possible to use other methods such as Linear discriminant analysis, LDA, that has been shown to be effective in extracting further information, Indahl et al. (1999, Chem. and Intell. Lab. Syst, 49, pl9-31). This approach is based on first decomposing the data using PLS-DA, and then using the scores vectors (instead of the original variables) as input to LDA. Further details on LDA can be found in Duda and Hart (Classification and Scene Analysis, 1973, Wiley, USA). The next step following model building is of model validation. This step is considered to be amongst the most important aspects of multivariate analysis, and tests the "goodness" of the calibration model which has been built. In this work, a cross validation approach has been used for validation. In this approach, one or a few samples are kept out in each segment while the model is built using a full cross-validation on the basis of the remaining data. The samples left out are then used for prediction/classification. Repeating the simple cross-validation process several times holding different samples out for each cross-validation leads to a so-called double cross-validation procedure. This approach has been shown to work well with a limited amount of data, as is the case in some of the Examples described here. Also, since the cross validation step is repeated several times the dangers of model bias and overfϊtting are reduced. Once a calibration model has been built and validated, genes exhibiting an expression pattern that is most relevant for describing the desired information in the model can be selected by techniques described in the prior art for variable selection, as mentioned elsewhere. Variable selection will help in reducing the final model complexity, provide a parsimonious model, and thus lead to a reliable model that can be used for prediction. Moreover, use of fewer genes for the puφose of providing diagnosis will reduce the cost of the diagnostic product.

In this way informative probes which would bind to the genes of relevance maybe identified. We have found that after a calibration model has been built, statistical techniques like Jackknife (Effron, 1982, The Jackknife, the Bootstrap and other resampling plans. Society for Industrial and Applied mathematics, Philadelphia, USA), based on resampling methodology, can be efficiently used to select or confirm significant variables (informative probes). The approximate uncertainty variance of the PLS regression coefficients B can be estimated by: M

m=l

where S²B = estimated uncertainty variance of B;

B = the regression coefficient at the cross validated rank A using all the N objects;

B_m = the regression coefficient at the rank A using all objects except the object(s) left out in cross validation segment m; and g = scaling coefficient (here: g=l). In our approach, Jackknife has been implemented together with cross-validation. For each variable the difference between the B-coefficients Bi in a cross-validated sub-model and Btot for the total model is first calculated.

The sum of the squares of the differences is then calculated in all sub-models to obtain an expression ofthe variance ofthe Bi estimate for a variable. The significance of the estimate of Bi is calculated using the t-test. Thus, the resulting regression coefficients can be presented with uncertainty limits that correspond to 2 Standard Deviations, and from that significant variables are detected. No further details as to the implementation or use of this step are provided here since this has been implemented in commercially available software, The Unscrambler, CAMO ASA, Norway. Also, details on variable selection using Jackknife can be found in Westad & Martens (2000, J. Near Inf.- Spectr., 8, pi 17-124). The following approach can be used to select informative probes from a gene expression data set: a) keep out one unique sample (mcluding its repetitions if present in the data set) per cross validation segment; b) build a calibration model (cross validated segment) on the remaining samples using PLSR-DA; c) select the significant genes for the model in step b) using the Jackknife criterion; d) repeat the above 3 steps until all the unique samples in the data set are kept out once (as described in step a). For example, if 75 unique samples are present in the data set, 75 different calibration models are built resulting in a collection of 75 different sets of significant probes; e) select the most significant variables using the frequency of occurrence criterion in the generated sets of significant probes in step d). For example, a set of probes appearing in all sets (100%) are more informative than probes appearing in only 50% of the generated sets in step d). Once the informative probes for a disease have been selected, a final model is made and validated. The two most commonly used ways of validating the model are cross-validation (CV) and test set validation. In cross-validation, the data is divided into k subsets. The model is then trained k times, each time leaving out one of the subsets from training, but using only the omitted subset to compute enor criterion, RMSEP (Root Mean Square Enor of Prediction). If k equals the sample size, this is called "leave-one-out" cross-validation. The idea of leaving one or a few samples out per validation segment is valid only in cases where the covariance between the various experiments is zero. Thus, one sample at-a-time approach can not be justified in situations containing replicates since keeping only one of the replicates out will introduce a systematic bias in our analysis. The conect approach in this case will be to leave out all replicates of the same samples at a time since that would satisfy assumptions of zero covariance between the CN-segments. The second approach for model validation is to use a separate test-set for validating the calibration model. This requires running a separate set of experiments to be used as a test set. This is the preferred approach given that real test data are available. The final model is then used to identify a cancer or stage thereof in test samples. For this puφose, expression data of selected informative genes is generated from test samples and then the final model is used to determine whether a sample belongs to a diseased or non-diseased class or has a cancer or stage thereof. Preferably a model for classification pmposes is generated by using the data relating to the probes identified according to the above described method. Preferably the sample is as described previously. Preferably the oligonucleotides which are immobilized in step (a) are randomly selected from within the family described hereinbefore, but alternatively may be selected to represent the different families, e.g. by selecting one or more of the oligonucleotides corresponding to genes encoding proteins with common functions in different families. Especially preferably, said selection is made to encompass oligonucleotides derived from genes of family (i) and (ii). Such oligonucleotides may be of considerable length, e.g. if using cDΝA (which is encompassed within the scope of the term "oligonucleotide"). The identification of such cDΝA molecules as useful probes allows the development of shorter oligonucleotides which reflect the specificity of the cDΝA molecules but are easier to manufacture and manipulate. The above described model may then be used to generate and analyse data of test samples and thus may be used for the diagnostic methods of the invention. In such methods the data generated from the test sample provides the gene expression data set and this is normalized and standardized as described above. This is then fitted to the calibration model described above to provide classification. The method described herein can also be used to simultaneously select informative probes for several cancers. Depending upon which cancers have been included in the calibration or training set, informative probes can be selected for the said cancers. The informative probes selected for one cancer may or may not be similar to the informative probes selected for another cancer of interest. It is the pattem with which the selected genes are expressed in relation to each other during a cancer or stage thereof, that determines whether or not they are informative for the cancer or stage thereof. In other words, informative genes are selected based on how their expression correlates with the expression of other selected informative genes under the influence of responses generated by the cancer or stage thereof under investigation. For the puφose of isolating informative probes or identifying several cancers and stages thereof simultaneously, the gene expression data set must contain the information on how genes are expressed when the subject has a particular cancer or stage thereof under investigation. The data set is generated from a set of healthy or diseased samples, where a particular sample may contain the information of only one cancer or stage thereof or may also contain information about multiple cancers or stages thereof. Hence, the method also teaches an efficient experimental design to reduce the number of samples required for isolating informative probes by selecting samples representing more than one cancer or stage thereof. As mentioned previously, in view of the high information content of most transcripts, the identification and selection of informative probes for use in diagnosing, monitoring or identifying a particular cancer or stage thereof may be dramatically simplified. Thus the pool of genes from which a selection may be made to identify informative probes may be radically reduced. Unlike, in prior art technologies where informative probes are selected from a population of thousands of genes that are being expressed in a cell, like in microanay, in the method described herein, the informative probes are selected from a limited number of genes as described in the gene sequence families described hereinbefore. From within these families, probes of interest may be randomly selected. Thus in a prefened aspect, said set of oligonucleotides are randomly selected from the primary oligonucleotides as described hereinbefore. As referred to herein "random" refers to selection which is not biased based on the extent of information carried by the transcripts in relation to the cancer or organism under study, ie. without bias towards their likely utility as informative probes. Whilst a random selection may be made from a pool of transcripts (or related products) which have been biased, e.g. to highly or moderately expressed transcripts, preferably random selection is made from a pool of transcripts not biased or selected by a sequence-based criterion. The larger set may therefore contain oligonucleotides conesponding to highly and moderately expressed genes, or alternatively, may be enriched for those corresponding to the highly and moderately express ed genes . Random selection from highly and moderately expressed genes can be achieved in a wide variety of ways. For example, by randomly picking a significant number of cDNA clones from a cDNA library constructed from a biological specimen under investigation containing clones corresponding to the gene sequence families described hereinbefore. Since, in a cDNA library, the cDNA clones conesponding to transcripts present in high or moderate amount are more frequently present than transcripts conesponding to cDNA present in low amount, the former will tend to be picked up more frequently than the latter. A pool of cDNA enriched for those conesponding to highly and moderately expressed genes can be isolated by this approach. To identify genes that are expressed in high or moderate amount among the isolated population for use in methods of the invention, the information about the relative level of their transcripts in samples of interest can be generated using several prior art techniques. Both non-sequence based methods, such as differential display or RNA fϊngeφrinting, and sequence-based methods such as microanays or macroanays can be used for the puφose. Alternatively, specific primer sequences for highly and moderately expressed genes can be designed and methods such as quantitative RT-PCR can be used to determine the levels of highly and moderately expressed genes. Hence, a skilled practitioner may use a variety of techniques which are known in the art for determining the relative level of mRNA in a biological sample. Especially preferably the sample for the isolation of mRNA in the above described method is as described previously and is preferably not from the site , of disease and the cells in said sample are not disease cells and have not contacted disease cells, for example the use of a peripheral blood sample for detection of non-haematopoietic cancer, e.g. breast cancer. The following examples are given by way of illustration only in which the Figures refened to are as follows: Figure 1 shows the possible inteφlay of various factors responsible for changes in expression in an individual with breast cancer; Figure 2 shows the projection of 102 normal (including benign) and breast cancer samples onto a classification model generated by PLSR-DA using the data of 35 informative genes, in which PC is the principal components and N and C are normal and breast cancer samples, respectively; Figure 3 shows a prediction plot based on 3 principal components using the data of 35 cDNAs; and Figure 4 shows the mean level of expression of the 35 genes used for prediction of breast cancer.

Example 1: Diagnosis of Breast Cancer

Methods

Blood samples

Blood samples were collected from donors with their informed consent under an approval from Regional Ethical Committee of Norway. All donors were treated anonymously during analysis. Blood was drawn from females with a suspect initial mammogram, which included both females with breast cancer and females with abnormal mammograms, prior to any knowledge of whether the abnormality observed during first screening was benign or malignant. In all cases, the blood samples were drawn between 8 a.m. and 4 p.m. From each female, 10 ml blood was drawn by skilled personnel either in vacutainer tubes containing EDTA as anticoagulant (Becton Dickinson, Baltimore, USA) or directly in PAXgene™ tubes (PreAnalytiX, Hombrechtikon, Switzerland).

Blood collected in EDTA tubes was immediately stored at - 80° C, while PAX tubes were left overnight and then stored at - 80°C until used.

Preparation ofcDNA arrays 1435 cDNA clones were randomly picked from a plasmid library constructed from whole blood of 550 healthy individuals (Clontech, Palo Alto, USA). About 20% of the randomly picked clones were redundant. For amplification of inserts, bacterial clones were grown in microtiter plates containing 150 μl LB with 50 μg/ml carbenicillin, and incubated overnight with agitation at 37°C. To lyse the cells, 5 μl of each culture were diluted with 50 μl H₂O and incubated for 12 min. at 95°C. Of this mixture, 2 μl were subjected to a PCR reaction using 40 μpmol of 5'- and 3'- sequencing primer in the presence of 1.5 mM MgCl₂. PCR reactions were performed with the following cycling protocol: 4 min. at 95°C, followed by 25 cycles of 1 min. at 94°C, 1 min. at 60°C and 3 min. at 72°C either in a RoboCycler® Temperature Cycler (Stratagene, La Jolla, USA) or

DNA Engine Dyad Peltier Thermal Cycler (MJ Research Inc., Waltham, USA). The amplified products were denatured with NaOH (0.2 M, final concentration) for 30 min and spotted onto Hybond-N^1" membranes (Amersham Pharmacia Biotech, Little Chalfont, UK), using a MicroGrid II workstation according to the manufacturer's instructions (BioRobotics Ltd, Cambridge England). The immobilized cDNAs were fixed using a UN cross-linker (Hoefer Scientific Instruments, San Francisco, USA). In addition to the 1435 cDΝAs, the printed arrays also contained controls for assessing background level, consistency and sensitivity of the assay. These were spotted at multiple positions and included controls such as PCR mix

(without any insert); controls of SpotReport™ 10 anay validation system (Stratagene, La Jolla, USA) and cDNAs conesponding to constitutively expressed genes such as β-actin, γ-actin, GAPDH, HOD and cyclophilin.

RNA extraction, probe synthesis and hybridization Blood collected in EDTA tubes was thawed at 37°C and transfened to PAX tubes, and total RNA was purified according to the supplier's instructions (PreAnalytiX, Hombrechtikon, Switzerland). From blood collected directly in PAX tubes total RNA was extracted in the tubes as above without any transfer to new tubes. Contaminating DNA was removed from the isolated RNA by DNAase I treatment using DNA-free kit (Ambion, Inc. Austin, USA). RNA quality was determined visually by inspecting the integrity of 28S and 18S ribosomal bands following agarose gel electrophoresis. Only samples from which good quality RNA was extracted were used in this study. In our experience, blood collected in EDTA tubes often resulted in poor quality RNA, while that collected in PAX tubes almost always gave good quality RNA. The concentration and purity of extracted RNA was determined by measuring the absorbance at 260 nm and 280 nm. From the total RNA, mRNA was isolated using Dynabeads according to the supplier's instructions (Dynal AS, Oslo, Norway). Labelling and hybridization experiments were performed in 16 batches .

The number of samples assayed in each batch varied from six to nine. To minimize the noise due to batch-to-batch variation in printing, only the arrays manufactured during the same print run were used in each batch. When samples were assayed more than once (replicates), aliquots from the same mRNA pool were used for probe synthesis. For probe synthesis, aliquots of mRNA corresponding to 4-5 μg of total RNA were mixed together with oligodT_25Nv (0.5 μg/μl) and mRNA spikes of SpotReport™ 10 anay validation system (10 pg; Spike 2, 1 pg), heated to 70°C, and then chilled on ice. Probes were prepared in 35 μl reaction mixes by reverse transcription in the presence of 50 μCi [α³³P] dATP, 3.5 μM dATP, 0.6 mM each of dCTP, dTTP, dGTP, 200 units of

Superscript reverse transcriptase (Invitrogen, LifeTechnologies) and 0.1 M DTT labelling for 1.5 hr at 42°C. Following synthesis, the enzyme was deactivated for 10 min. at 70°C and mRNA removed by incubating the reaction mix for 20 min. at 37°C in 4 units of Ribo H (Promega, Madison USA). Unincoφorated nucleotides were removed using ProbeQuant G 50 Columns (Amersham Biosciences, Piscataway, USA). The membranes were equilibrated in 4 x SSC for 2 hr at room temperature and prehybridized overnight at 65° C in 10 ml prehybridization solution (4 x SSC, 0.1 M NaH₂PO_4j 1 mM EDTA, 8% dextran sulphate, 10 x Denhardt's solution, 1% SDS). Freshly prepared probes were added to 5 ml of the same prehybridization solution, and hybridization continued overnight at 65°C. The membranes were washed at 65°C with increasing stringency (2 x 30 min. each in 2 x SSC, 0.1% SDS; 1 x SSC, 0.1% S DS; 0.1 x SSC, 0.1% SDS).

Quantification of hybridization signals

The hybridized membranes were exposed to Phosphoscreen (super resolution) for two days and an image file generated using Phospholmager (Cyclone, Packard, Meriden, USA). The identification and quantification of the hybridization signals, as well as subtraction of local background values was performed using Phoretix software (Non Linear Dynamics, UK). For background subtraction, the median of the line of pixels around each spot outline was subtracted from the intensity of the signals assessed in each spot.

Data analysis

From the 1435 background-subtracted expression data, signals of 67 genes were removed from each membrane to exclude genes expressed with a high degree of variance. These included removal of 1.25% of the lowest and highest signals from each membrane. For normalization, the value of each spot was first divided by the mean of signals in each anay followed by a cube root transformation of all the spots. The normalized data was then batch adjusted using a one-way analysis of variance (ANONA).

The pre-processed data was then used to isolate the informative probes by: a) building a crossvalidated PLSR model, where one unique sample (including all repetitions of the selected sample) was kept out per cross- validation segment.

b) selecting the set of significant genes for the model in step a) using the Jackknife criterion.

c) building a crossvalidated PLSR-DA model as in step a) using the gene selected in step b). d) selecting again the set of most significant genes for the model in step c) using the Jackknife criterion.

Step b) resulted in 125 genes.

Step d) resulted in selection of 35 significant genes. Based on these genes a final classification model was constructed.

The selected informative probes based on occurrence criterion were used to constract a classification model. The result of the classification model based on 35 probes is shown in Figure 2 in which it is seen that the expression pattern of these genes was able to classify most women with breast cancer and women with no breast cancer into distinct groups. In this figure PCI and PC2 indicate the two principal components statistically derived from the data which best define the systemic variability present in the data. This allows each sample, and the data from each of the informative probes to which the sample's labelled first strand cDNA was bound, to be represented on the classification model as a single point which is a projection of the sample onto the principal components - the score plot.

Figure 3 shows the prediction plot using the 35 significant genes. In the prediction plot shown, the cancer samples appear on the x axis at +1 and the non-cancer samples appear at -1. The y axis represents the predicted class membership. During prediction, if the prediction is correct, cancer samples should fall above zero and non-cancer samples should fall below zero. In each case almost all samples are correctly predicted. For cross-validation 102 experimental samples were divided into 60 cross-validation segments where each segment represented one unique sample and included its replicates if present.

Conect prediction of most breast cancer cells was achieved. 19 out of 22 cancer patients were conectly predicted as were 34/35 normal patients. Full details of the individuals examined and the accuracy of prediction are shown in Table 1. Table 2 provides details of the 35 informative genes, the genes in public databases to which they show sequence similarity and their putative biological function. Their sequences follow these examples.

Figure 4 shows the expression level of the 35 genes and it will be seen that some are over-expressed and others under-expressed relative to expression in normal patients.

Example 2: Identification of further informative probes and use in diagnosis of breast cancer

Methods

The methods of identification and analysis used were essentially as described in Example 1 , except that instead of preparing a cDNA anay, samples were analysed using a commercially available platform for large-scale gene expression analysis (Agilent 22K chip).

A larger number of samples comprising 122 in total (78 control and 44 with breast cancer) were analysed. The data was analysed using PLSR as described previously. The genes of interest were selected by a 10-fold cross validation approach. Thus, the data from 122 samples was divided into 10 sets, each set containing 12-13 samples. A calibration model was built on 9 sets leaving out one set. Significant genes were identified by the Jackknife technique on the built-in model. These steps were repeated for all 10 sets, in which each set was kept out at least once. Informative genes were then identified based on the frequency of occunence criterion. 109 genes were found to informative in all 10 calibration models.

Results The above described 109 genes and 3 further genes were used to predict the classification of 122 of the samples used. The results are shown in the table below.

The 109 informative genes may be divided into three categories, namely those falling into families (i) and (ii) as described herein and other genes. Table 3 provides details of the informative probes whose corresponding genes fall into families (i) and (ii) and provides the number assigned by Agilent to that probe. Table 4 similarly provides details of the informative probes whose corresponding genes do not appear to fall within families (i) and (ii). Tables 5 and 6 provide details of the genes to which the probes in Tables 3 and 4, respectively, show sequence similarity, their putative biological function where known and the accession numbers for those genes. Appendix A

Partial Least Squares regression (PLSR)

Let a multivariate regression model be defined as:

Y= XB + F

where X a NxP matrix with N predictor variables (genes) ;

Y (NxJ) being the Jpredicted variables. In our case Y represents a matrix containing dummy variables;

B is a matrix of regression coefficients; and

F is a NxJ matrix of residuals.

The structure of the PLSR model can be written as:

X = TP^T + E_A, and Y = TQ^T + F_A, where

where

T (NxA) is a matrix of score vectors which are linear combinations of the x-variables;

P (PxA) is a matrix with the x-loading vectors p_a as columns; Q (JxA) is a matrix with the y-loading vectors q_a as columns;

E_a (NxP) is the matrix for X after A factors; and F_a (NxJ) is the matrix for Y after A factors.

The criterion in PLSR is to maximize the explained covariance of [X,Y]. This is achieved by the loading weights vector w_a+ι, which is the first eigenvector of

E_a ^TF_aFa^TE_a (E_a and F_a are the deflated X and Y after a factors or PLS components). The regression coefficients are given by: B = W(P^T )^"1Q^T

A PLSR model with full rank, i.e. maximum number of components, is equivalent to the MLR solutions. Further details on PLSR can be found in

Marteus & Naes, 1989, Multivariate Calibration, John Wiley & Sons, Inc., USA andKowalski & Seasholtz, 1991, supra.

Nucleotide Sequences of 34/35 genes selected by Jackknife

Clone Ids and their sequences

1-30

CTTTTCCTCCCGCTGTCCCCCACGGAGGGGACTGCTCTCCCCCGCTGCATCCTT TCTGTGAGGTACCTTACCCACCTCAGCACCTGAGAGGGTGAAATAGAATTCTAA CCTCGACATTCGGGAAGTGTTTTTGAGAAGTCTCGGTCGGTAAGGGAAGTCTTC CAAGTCCGTGCAGCACTAACGTATTGGCACCTGCCTCCTCTTCGGCCACCCCCC AGATG AGGCAGCTGTGACTGTGTCAAGGG AAGCCACGACTCTGACCATAGTCTT

CTCTCAGCTTCCACTGCCGTCTCCACAGGAAACCCAGAAGTTCTGTGAACAAGT CCATGCTGCCATCAAGGCATTTATTGCAGTGTACTATTTGCTTCCAAAGGATCA GGCCCTGAGAACAATGACCTTATTTCCTACAACAGTGTCTGGGTTGCGTGCCAG CAGATGCCTCAGATACCAAGAGATAACAAAGCTGCAGCTCTTTTGATGCTGACC AAGAATGTGGATTTTGTGAAGGATGCACATGAAGAAATGGAGCAGGCTGTGGAA

GAATGTGACCCTTACTCTGGCCTCTTGAATGATACTGAGGAGAACAACTCTGAC AACCACAATCATGAGGATGATGTGTTGGGGTTTCCCAGCAATCAGGACTTGTAT TGGTCAGAGGACGATCAAGAGCTCATAATCCCATGCCTTGCGCTGGTGAGAGCA TCCAAAGCCTGCCTGAAGAAAATTCGGATGTTAGTGGCAGAGAATGGGAAGAAG GATCAGGTGGCACAGCTGGATGACATTGTGGATATTTCTGATGAAATCAGCCCT

AGTGTGGATGATTTGGCTCTGAGCATATATCCACCTATGTGTCACCTGACCGTG CGAATCAATTCTGCGAAACTTGTATCTGTTTTAAAGAAGGCACTTGAAATTACA AAAGCAAGTCATGTGACCCCTCAGCCAGAAGATAGTTGGATCCCTTTACTTATT AATGCCATTGATCATTGCATGAATAGAATCAAGGAGCTCACTCAGAGTGAACTT GAATTATGACTTTTCAGGCTCATTTGTACTCTCTTCCCCTCTCATCGTCATGGT

CAGGCTCTGATACCTGCTTTTAAAATGGAGCTAGAATGCTTGCTGGATTGAAAG GGAGTGCCTATCTATATTTAGCAAGAGACACTATTACCAAAGATTGTTGGTTAG GCCAGATTGACACCTATTTATAAACCATATGCGTATATTTTTCTGTGCTATATA TGAAAAATAATTGCATGATTTCTCATTCCTGAGTCATTTCTCAGAGATTCCTAG GAAAGCTGCCTTATTCTCTTTTTGCAGTAAAGTATGTTGTTTTCATTGTAAAGA

TGTTGATGGTCTCAATAAAATGCTAACTTGCCAGTGAAAAAAAAAAAAAA

AGGATCTAAGACCAGCCTGGCAGCCACCAGATGGTGATTCTAGTCCTGGCTCAG TCAGT AATAGGTCACTGACCCCAGAGAAATCAATTCAGCCTCCCCAGGTCCTTG

GATTTCTTTCTGTGAAAATGAAAGCATAGGTAGGAATTTCCCATGGAACAGCTA

GCAGAGGAGAAATATTAAAAGTCAGGAGACTCATGCTATAGTTTTCATACTTCA TTACAACAATGTTGTTTAGGACAAGTGAGTTAACCTGTTAGCTTCCTCTATATA AAATGGAAAGTCATTAAAAACCTACATAGCAGGGTTCTTGTGAAGATCAAGTGA TAATGTAGGAAGCATGTACAAATGTCACATTCTGCCGTCACGTAATGGTCCTCA CAGCTTGAGGTAGCATTTAGCATGTGTCATGATTTAGTACAAGGGTTGGCAAAC TGTTGCTCTTGGATTAAGTCTGGCTCATTGCCTGTTTTTCAAAGAAAAAAATTG

TATATGTGTGTATATATGTTATATATAGGTACACACACATATGTGCTATATATA GCATATATACACACATAATATATAAACATGTACATATATAGCATTATATATATA CGTGTATAATATCTCCAGTCCTCATGACCAGCCATGCTTGTTCATTTACATTTG CATACTCTATGATTGCTTTCATGCAACAATGGCAGAGTTGAGTGATTGTTTTGC AACAGAGACTGTATGGCCCACTAAACCTAAAATATTTAGTCTCTGACCCTGAAA

TGTAAGATTGATAGCCCAGGACCAGGCGTGGTGGCTCACACTTGTAATCCTAGC ACTTTGGCAGGCCAAGGAGGGTGGATCACCTGAGGTCAGGAGTTAAAGACCAGC CTGGCCAACATGGTGAAACCCTGACTCTACTAAAAATACAGAAATTAGCTGGGC GTGGTAATGGGTGCCTGCAATCCAAGCTACTCTGGAGGCTGAGGCAGGAGAATC ACTTGAACCCAGGAGGCAGAAGTTACAGTGAGCTGAGATGGTGCCACTGCACTC

CAGCCTGGACGACAGAGTGAGACTCCATCTCAAAAA

iπ-27

CCATTCTCCTGCCTCAGCCTCTCAAGTAGCTGGGACTACAGGCGCCCACAACCA CGCCCGGCTAATGTTTTTGGTATTTTTCGTAGAGACGGGGTTTCACCTTGTTAG

CCAGGATGGTCTTGATCTCCTGACCTCGTGATCTGCCTGCCTCGGCCTCCCAAA GTGTTGGGATTACAGGCACATTTTTCACAATTTTTTAACACTTAAGAATGACTT AACTGAATCATGCCTTTAGAAGAAACTTTCTGTTTAAAAAAAAAAAAAAA

CTGCCGCCGCCCCCAGCTCCCCCGCCTCGGGGAGGGCACCAGGTCACTGCAGCC AGAGGGGTCCAGAAGAGAGAGGAGGCACTGCCTCCACTACAGCAACTGCACCCA CGATGCAGAGCATCAAGTGCGTGGTGGTGGGTGATGGGGCTGTGGGCAAGACGT GCCTGCTCATCTGCTACACAACTAACGCTTTCCCCAAAGAGTACATCCCCACCG TGTTCGACAATTACAGCGCGCAGAGCGCAGTTGACGGGCGCACAGTGAACCTGA

ACCTGTGGGACACTGCGGGCCAGGAGGAGTATGACCGCCTCCGTACACTCTCCT ACCCTCAGACCAACGTTTTCGTCATCTGTTTCTCCATTGCCAGTCCGCCGTCCT ATGAGAACGTGCGGCACAAGTGGCATCCAGAGGTGTGCCACCACTGCCCTGATG TGCCCATCCTGCTGGTGGGCACCAAGAAGGACCTGAGAGCCCAGCCTGACACCC TACGGCGCCTCAAGGAGCAGGGCCAGGCGCCCATCACACCGCAGCAGGGCCAGG

CACTGGCCAAGCAGATCCACGCTGTGCGCTACCTCGAATGCTCAGCCCTGCAAC AGGATGGTGTCAAGGAAGTGTTCGCCGAGGCTGTCCGGGCTGTGCTCAACCCCA CGCCGATCAAGCGTGGGCGGTCCTGCATCCTCTTGTGACCCTGGCACTTGGCTT GGAGGCTGCCCCTGCCCTCCCCCCACCAGTTGTGCCTTGGTGCCTTGTCCGCCT CAGCTGTGCCTTAAGGACTAATTCTGGCACCCCTTTCCAGGGGGTTCCCTGAAT GCCTTTTTCTCTGAGTGCCTTTTTCTCCTTAAGGAGGCCTGCAGAGAAAGGGGC TTTGGGCTCTGCCCCCCTCTGCTTGGGAACACTGGGTATTCTCATGAGCTCATC

CAAGCCAAGGTTGGACCCCTCCCCAAGAGGCCAACCCAGTGCCCCCTCCCATTT TCCGTACTGACCAGTTCATCCAGCTTTCCACACAGTTGTTGCTGCCTATTGTGG TGCCGCCTCAGGTTAGGGGCTCTCAGCCATCTCTAACCTCTGCCCTCGCTGCTC TTGGAATTGCGCCCCCAAGATGCTCTCTCCCTTCTCCAATGAGGGAGCCACAGA ATCCTGAGAAGGTGAATGTGCCCTAACCTGCTCCTCTGTGCCTAGGCCTTACGC

ATTTGCTGACTGACTCAGCCCCCATGCTTCTGGGGACCTTTCCTACCCCCATCA GCATCAATAAAACCTCCTGTCTCCAGTGA

IN-26 CAGCCCTCCGTCACCTCTTCACCGCACCCrCGGACTGCCCCAAGGCCCCCGCCG

CCGCTCCAGCGCCGCGCAGCCACCGCCGCCGCCGCCGCCTCTCCTTAGTCGCCG CCATGACGACCGCGTCCACCTCGCAGGTGCGCCAGAACTACCACCAGGACTCAG AGGCCGCCATCAACCGCCAGATCAACCTGGAGCTCTACGCCTCCTACGTTTACC TGTCCATGTCTTACTACTTTGACCGCGATGATGTGGCTTTGAAGAACTTTGCCA AATACTTTCTTCACCAATCTCATGAGGAGAGGGAACATGCTGAGAAACTGATGA

AGCTGCAGAACCAACGAGGTGGCCGAATCTTCCTTCAGGATATCAAGAAACCAG ACTGTGATGACTGGGAGAGCGGGCTGAATGCAATGGAGTGTGCATTACATTTGG AAAAAAATGTGAATCAGTCACTACTGGAACTGCACAAACTGGCCACTGACAAAA ATGACCCCCATTTGTGTGACTTCATTGAGACACATTACCTGAATGAGCAGGTGA AAGCCATCAAAGAATTGGGTGACCACGTGACCAACTTGCGCAAGATGGGAGCGC

CCGAATCTGGCTTGGCGGAATATCTCTTTGACAAGCACACCCTGGGAGACAGTG ATAATGAAAGCTAAGCCTCGGGCTAATTTCCCCATAGCCGTGGGGTGACTTCCC TGGTCACCAAGGCAGTGCATGCATGTTGGGGTTTCCTTTACCTTTTCTATAAGT TGTACCAAAACATCCACTTAAGTTCTTTGATTTGTACCATTCCTTCAAATAAAG AAATTTGGTACCCAAAAAAAA

IN-41

GCCATTTCTAAGACCTACAGCTACCTGACCCCCGACCTCTGGAAGGAGACTGTA TTCACCAAGTCTCCCTATCAGGAGTTCACTGACCACCTCGTCAAGACCCACACC AGAGTCTCCGTGCAGCGGACTCAGGCTCCAGCTGTGGCTACAACATAGGGTTTT

TATACAAGAAAAATAAAGTGAATTAAGCGTGAAAA IV-51 ATTTCTGTGGATACAGTGCCCACCGCCCTCCTCCACTTGGAAACGGTATCCTCC CTGCCCATCCGTCTGTCTGTCGCCCTTCTCCCGGCCCTCACTAAGCCCCGGCAC TTCTAGTGGTCTCACCTGGAGGCAAGAGGGAGGGGACAGAGGCCCTGCCACGTC CCGCTGCCTCCTGCTCTCTGGAGGTACTGAGACAGGGTGCTGATGGGAAGGAGG GGAGCCTTTGGGGGGCCACCCGGGGCCTGGACCTATGCAGGGAGGCCACGTCCC

ACCCCACCTCTTGTTTCTGGGTCCCTGCTCCCCTTTGGGGGTGTGTGTGTGTGT TTTAATTTTCTTTATGGAAAAATTGACAAAAAAAAATAGAGAGAGAGGTATTTA ACTGCAATAAACTGGCCCCATGTGGCCCCCGCCTTGTCAAAAAAAAAA

N-09

TGGATTCCCGTCGTAACTTAAAGGGAAACTTTCACAATGTCCGGAGCCCTTGAT GTCCTGCAAATGAAGGAGGAGGATGTCCTTAAGTTCCTTGCAGCAGGAACCCAC TTAGGTGGCACCAATCTTGACTTCCAGATGGAACAGTACATCTATAAAAGGAAA AGTGATGGCATCTATATCATAAATCTCAAGAGGACCTGGGAGAAGCTTCTGCTG GCAGCTCGTGCAATTGTTGCCATTGAAAACCCTGCTGATGTCAGTGTTAT ATCC

TCCAGGAATACTGGCCAGAGGGCTGTGCTGAAGTTTGCTGCTGCCACTGGAGCC ACTCCAATTGCTGGCCGCTTCACTCCTGGAACCTTCACTAACCAGATCCAGGCA GCCTTCCGGGAGCCACGGCTTCTTGTGGTTACTGACCCCAGGGCTGACCACCAG CCTCTCACGGAGGCATCTTATGTTAACCTACCTACCATTGCGCTGTGTAACACA GATTCTCCTCTGCGCTATGTGGACATTGCCATCCCATGCAACAACAAGGGAGCT

CACTCAGTGGGTTTAATGTGGTGGATGCTGGCTCGGGAAGTTCTGCGCATGCGT GGCACCATTTCCCGTGAACACCCATGGGAGGTCATGCCTGATCTGTACTTCTAC AGAGATCCTGAAGAGATTGAAAAAGAAGAGCAGGCTGCTGCTGAGAAGGCAGTG ACCAAGGAGGAATTTCAGGGTGAATGGACTGCTCCCGCTCCTGAGTTCACTGCT ACTCAGCCTGAGGTTGCAGACTGGTCTGAAGGTGTACAGGTGCCCTCTGTGCCT

ATTCAGCAATTCCCTACTGAAGACTGGAGCGCTCAGCCTGCCACGGAAGACTGG TCTGCAGCTCCCACTGCTCAGGCCACTGAATGGGTAGGAGCAACCACTGACTGG TCTTAAGCTGTTCTTGCATAGGCTCTTAAGCAGCATGGAAAAATGGTTGATGGA AAATAAACATCAGTTTCT

N-38

GTTTAAATTTGACAAACTAAAGCTAATTACTGCTATAAGAGTAATAACTGCTCA TTTTCCATAACTCATTCTTAAAGTTTTAGTAATGTAAAAGTTATTTTTTTGCAG TAAGTTATAATGATAGAAGCTTACATGTTTTTTCATGCCTCATCTGTTTCCCCT TAAAACTATAATTATCAGT AAAGTCCTGTGGTATTTTTCAATTTGTAAGAAACT

AGGCTATATATACATTGGGAAAAACAGCCTTCATTTGTCAATGCACTAGTGTTC CAAAGGTTTCTGGTAATTGTGTGCTATTGCTTTTTGTTGACTTGCAAAAAAAAA AAAAAAAAAATTACTATGACTTGTGGTAGCCCTGCAACCTTCGGAAGTGCTTAG CCCAGTCTGACCATACATTTATATTTAGAATGCTTAGGTAAATAAATAATATGC CTAAACCCAATGCTATAAGATACTATATAATATCTCATAATTTTAAAAATCACT GTTTTGTATAATAATAAAACAAGGCAGGCAAGCTGTTCTACAATGACTGTTGGT AAGGGTGCTGAGGAAGAAAAACAAACAATCTTGATTCAGGGATAGTGAATAGAC

AAAAAATGTCCTAATCAATGAAGCTGTGTGATGATTCTGATTGACAGAGAGTGC TGCCACAAGATTCTTAGGCTACACTCAAATCAGCAGAAAAAGTGCTACAATAAA TTAGAAGTGACTATTACAGGTGCAGATGAGGGTTGGTAGTACCTGTTTGCCATT TCTCTTCTAATCTTATATTTTCTGACCCTCCTACTGTAAGTCGCGCGGAGGCGG AGGCTTGGGTGCGTTCAAGATTCAACTTCACCCGTAACCCACCGCCATGGCCGA

GGAAGGCATTGCTGCTGGAGGTGTAATGGACGTTAATACTGCTTTACAAGAGGT TCTGAAGACTGCCCTCATCCACGATGGCCTAGCACGTGGAATTCGCGAAGCTGC CAAAGCCTTAGACAAGCGCCAAGCCCATCTTTGTGTGCTTGCATCCAACTGTGA TGAGCCTATGTATGTCAAGTTGGTGGAGGCCCTTTGTGCTGAACACCAAATCAA CCT AATT AAGGTTGATGACAACAAGAAACTAGGAGAATGGGTAGGCCTTTGTAA

AATTGACAGAGAGGGGAAACCCCGTAAAGTGGTTGGTTGCAGTTGTGTAGTAGT TAAGGACTATGGCAAGGAGTCTCAGGCCAAGGATGTCATTGAAGAGTATTTCAA ATGCAAGAAATGAAGAAATAAATCTTTGGCTCACAAA

NI-44

GAGAATGGCTTGAACCCAGTAGGCAGAGGTTGTAGTGAGCCGAGATTGGGCCAC TGCACTTTAGCCTGGGTGACAGAGTGAGACTCTGTCTCAAAAAAAAAAAAAAAA AATTTAAATAAAATAAAAAACCTTTACTTATTTTTAAATTGGGTTGTCTTTTTG GTATTGAGTTGTTAAAGTTCTTTATATATTTTAGGTACAAATCCCTTATGAGAT ACGTGATTTGAAAATATTTTCTCCCATTCTGTGGGTTGCTTTTTCACTTTCTTG

GTTGTATCCTTTGAAGCACAGAAGTTTTAAATTTTGATGAAGTCCAGTTTATTT ATTTTTTTGCTGTTGTTTCTGCTCATACTTTTGAGGTCATGTCTGAGAAACCAT TGTCAAATCCAAGGTCGTGATGACTTACCCCTGTGT TTCTTCTAAGAGTTTTA AAGGCATCTGAAGCTTAATGTGCACTAGATGGATTCTAAATATCATCTCATCCA AAACCTGCTATATATACTACCTTCCTCATCTCAGTTGAAGGCAAGTCCATTGTT

TCAATTGCCTGGGCAAAAAATATTCTAAATAATTCATAATTTTTCCTCAACTCC ACATCTATTGGTAAATCCTGTGGGTTCTCCTTTTAAAACATATCCAAAATAGAA TCATTTCTCACTATCATTCCACTGCAGGCACCAAGTCTCAATAGTCTCCTAGCA GATAATCATGTCTACATTTATTCTCAATGTAGCAGCTAGAGAGCTTTTTTG

NI-49 GCGGTCGTAAGGGCTGAGGATTTTTGGTCCGCACGCTCCTGCTCCTGACTCACC GCTGTTCGCTCTCGCCGAGGAACAAGTCGGTCAGGAAGCCCGCGCGCAACAGCC ATGGCTTTTAAGGATACCGGAAAAACACCCGTGGAGCCGGAGGTGGCAATTCAC CGAATTCGAATCACCCTAACAAGCCGCAACGTAAAATCCTTGGAAAAGGTGTGT GCTGACTTGATAAGAGGCGCAAAAGAAAAGAATCTCAAAGTGAAAGGACCAGTT CGAATGCCTACCAAGACTTTGAGAATCACTACAAGAAAAACTCCTTGTGGTGAA

GGTTCTAAGACGTGGGATCGTTTCCAGATGAGAATTCACAAGCGACTCATTGAC TTGCACAGTCCTTCTGAGATTGTTAAGCAGATTACTTCCATCAGTATTGAGCCA GGAGTTGAGGTGGAAGTCACCATTGCAGATGCTTAAGTCAACTATTTTAATAAA TTGATGACCAGTTGTTAAAAAAAAAAAAAAA

NI-52

GAAAAGGGΝTΝGt^CCCAAΝGGGCAGAGGTTGGGCTGATGCCGATATTGGGCCΝ CTGCΝC CA ACCTGGGTGACATGAATGAAACTCTGTCTCACATAAAAACCCA AAAAAΝCTAAATGAAATAAAAGACCTTTGCTTATTΝCTAAΝTTGGGTACGC

VII- 15

CCCATCCCCTCGACCGCTCGCGTCGCATTTGGCCGCCTCCCTACCGCTCCAAGC CCAGCCCTCAGCCATGGCATGCCCCCTGGATCAGGCCATTGGCCTCCTCGTGGC CATCTTCCACAAGTACTCCGGCAGGGAGGGTGACAAGCACACCCTGAGCAAGAA GGAGCTGAAGGAGCTGATCCAGAAGGAGCTCACCATTGGCTCGAAGCTGCAGGA

TGCTGAAATTGCAAGGCTGATGGAAGACTTGGACCGGAACAAGGACCAGGAGGT GAACTTCCAGGAGTATGTCACCTTCCTGGGGGCCTTGGCTTTGAT

VII-32 AATTAGAGAGGTGAGGATCTGGTATTTCCTGGACTAAATTCCCCTTGGGGAAGA

CGAAGGGATGCTGCAGTTCCAAAAGAGAAGGACTCTTCCAGAGTCATCTACCTG AGTCCCAAAGCTCCCTGTCCTGAAAGCCACAGACAATATGGTCCCAAATGACTG ACTGCACCTTCTGTGCCTCAGCCGTTYTTGACATCAAGAATCTTCTGTTCCACA TCCACACAGCCAATACAATTAGTCAAACCACTGTTATTAACAGATGTAGCAACA TGAGAAACGCTT ATGTTACAGGTT ACATGAGAGCAATCATGTAAGTCTATATGA

CTTCAGAAATGTTAAAATAGACTAACCTCTAACAACAAATTAAAAGTGATTGTT TCAAGGTGATGCAATTATTGATGACCTATTTTATTTTTCTATAATGATCATATA TTACCTTTGTAATAAAACATTATAACCAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAA VII-48

CTTAAGTATGCCCTGACAGGAGATGAAGTAAAGAAGATTTGCATGCAGCGGTTC ATTAAAATCGATGGCAAGGTCCGAACTGATATAACCTACCCTGCTGGATTCATG GATGTCATCAGCATTGACAAGACGGGAGAGAATTTCCGTCTGATCTATGACACC AAGGGTCGCTTTGCTGTACATCGTATTACACCTGAGGAGGCCAAGTACAAGTTG

TGCAAAGTGAGAAAGATCTTTGTGGGCACAAAAGGAATCCCTCATCTGGTGACT CATGATGCCCGCACCATCCGCTACCCCGATCCCCTCATCAAGGTGAATGATACC ATTCAGATTGATTTAGAGACTGGCAAGATTACTGATTTCATCAAGTTCGACACT GGTAACCTGTGTATGGTGACTGGAGGTGCTAACCTAGGAAGAATTGGTGTGATC ACCAACAGAGAGAGGCACCCTGGATCTTTTGACGTGGTTCACGTGAAAGATGCC

AATGGCAACAGCTTTGCCACTCGACTTTCCAACATTTTTGTTATTGGCAAGGGC AACAAACCATGGATTTCTCTTCCCCGAGGAAAGGGTATCCGCCTCACCATTGCT GAAGAGAGAGACAAAAGACTGGCGGCCAAACAGAGCAGTGGGTGAAATGGGTCC CTGGGTGACATGTCAGATCTTTGTACGTAATTAAAAATATTGTGGCAGGATTAA TAGCC

VII-76

AGACACACGAGCATATTTCACCTCCGCTACCATAATCATCGCTATCCCCACCGG

CGTCAAAGTATTTAGCTGACTCGCCACACTCCACGGAAGCAATATGAAATGATC TGCTGCAGTGCTCTGAGCCCTAGGATTCATCTTTCTTTTCACCGTAGGTGGCCT

GACTGGCATTGTATTAGCAAACTCATCACTAGACATCGTACTACACGACACGTA CTACGTTGTAGCCCACTTCCACTATGTCCTATCAATAGGAGCTGTATTTGCCAT CATAGGAGGCTTCATTCACTGATTTCCCCTATTCTCAGGCTACACCCTAGACCA AACCTACGCCAAAATCCATTTCACTATCATATTCATCGGCGTAAATCTAACTTT CTTCCCACAACACTTTCTCGGCCTATCCGGAATGCCCCGACGTTACTCGGACTA

CCCCGATGCATACACCACATGAAACATCCTATCATCTGTAGGCTCATTCATTTC TCTAACAGCAGTAATATTAATAATTTTCATGATTTGAGAAGCCTTCGCTTCGAA GCGAAAAGTCCTAATAGTAGAAGAACCCTCCATAAACCTGGAGTGACTATATGG ATGCCCCCCACCCTACCACACATTCGAAGAACCCGTATACAT

IX-24

AGAGTGCAAGACGATGACTTGCAAAATGTCGCAGCTGGAACGCAACATAGAGAC

CATCATCAACACCTTCCACCAATACTCTGTGAAGCTGGGGCACCCAGACACCCT

GAACCAGGGGGAATTCAAAGAGCΓGGTGCGAAAAGATCTGCAAAATTTTCTCAA GAAGGAGAAT AAGAATGAAAAGGTCAT AGAACACATCATGGAGGACCTGGACAC

AAATGCAGACAAGCAGCTGAGCTTCGAGGAGTTCATCATGCTGATGGCGAGGCT AACCTGGGCCTCCCACGAGAAGATGCACGAGGGTGACGAGGGCCCTGGCCACCA CCATAAGCCAGGCCTCGGGGAGGGCACCCCCTAAGACCACAGTGGCCAAGATCA CAGTGGCCACGGCCACGGCCACAGTCATGGTGGCCACGGCCACAGCCACTAATC AGGAGGCCAGGCCACCCTGCCTCTACCCAACCAGGGCCCCGGGGCCTGTTATGT CAAACTGTCTTGGCTGTGGGGCTAGGGGCTGGGGCCAAATAAAGTCTCTTCCTC CAAAAAAAA

IX-39

CTTGGCTCCTGTGGAGGCCTGCTGGGAACGGGACTTCTAAAAGGAACTATGTCT

GGAAGGCTGTGGTCCAAGGCCATTTTTGCTGGCTATAAGCGGGGTCTCCGGAAC CAAAGGGAGCACACAGCTCTTCTTAAAATTGAAGGTGTTTACGCCCGAGATGAA

ACAGAATTCTATTTGGGCAAGAGATGCGCTTATGTATATAAAGCAAAGAACAAC ACAGTCACTCCTGGCGGCAAACCAAACAAAACCAGAGTCATCTGGGGAAAAGTA ACTCGGGCCCATGGAAACAGTGGCATGGTTCGTGCCAAATTCCGAAGCAATCTT CCTGCTAAGGCCATTGGACACAGAATCCGAGTGATGCTGTACCCCTCAAGGATT TAAACTAACGAAAAATCAATAAATAAATGTGGATTTGTGCTCTTGTA

IX-46

ACGCGAGATGGCAGTGCAAATATCCAAGAAGAGGAAGTTTGTCGCTGATGGCAT

CTTCAAAGCTGAACTGAATGAGTTTCTTACTCGGGAGCTGGCTGAAGATGGCTA CTCTGGAGTTGAGGTGCGAGTTACACCAACCAGGACAGAAATCATTATCTTAGC

CACCAGAACACAGAATGTTCTTGGTGAGAAGGGCCGGCGGATTCGGGAACTGAC TGCTGTAGTTCAGAAGAGGTTTGGCTTTCCAGAGGGCAGTGTAGAGCTTTATGC TGAAAAGGTGGCCACTAGAGGTCTGTGTGCCATTGCCCAGGCAGAGTCTCTGCG TTACAAACTCCTAGGAGGGCTTGCTGTGCGGAGGGCCTGCTATGGTGTGCTGCG GTTCATCATGGAGAGTGGGGCCAAAGGCTGCGAGGTTGTGGTGTCTGGGAAACT

CCGAGGACAGAGGGCTAAATCCATGAAGTTTGTGGATGGCCTGATGATCCACAG CGGAGACCCTGTTAACTACTACGTTGACACTGCTGTGCGCCACGTGTTGCTCAG ACAGGGTGTGCTGGGCATCAAGGTGAAGATCATGCTGCCCTGGGACCCAACTGG TAAGATTGGCCCTAAGAAGCCCCTGCCTGACCACGTGAGCATTGTGGAACCCAA AGATGAGATACTGCCCACCACCCCCATCTCAGAACAGAAGGGTGGGAAGCCAGA GCCGCCTGCCATGCCCCAGCCAGTCCCCACAGCATAACAGGGTCTCCTTGGCAG CTGTATTCTGGAGTCTGGATGTTGCTCTCTAAAGACCTTTAATAAAATTTTGT

IX-50 GTCCATCCTGCAGGCCACAAGCTCTGGATGAGGAACTTGAGGCAAGTCACCAGC

CCCTGATCATTTCGCCTAAAAGAGCAAGGACTAGAGTTCCTGACCTCCAGGCCA GTCCCTGATCCCTGACCTAATGTTATCGCGGAATGATGATATATGTATCTACGG GGGCCTGGGGCTGGGCGGGCTCCTGCTTCTGGCAGTGGTCCTTCTGTCCGCCTG

CCTGTGTTGGCTGCATCGAAGAGTAAAGAGGCTGGAGAGGAGCTGGGCCCAGGG

CTCCTCAGAGCAGGAACTCCACTATGCATCTCTGCAGAGGCTGCCAGTGCCCAG

CAGTGAGGGACCTGACCTCAGGGGCAGAGACAAGAGAGGCACCAAGGAGGATCC AAGAGCTGACTATGCCTGCATTGCTGAGAACAAACCCACCTGAGCACCCCAGAC

ACCTTCCTCAACCCAGGCGGGTGGACAGGGTCCCCCTGTGGTCCAGCCAGTAAA

AACCATGGTCCCCCCACTTCTGTGTCTCAGTCCTCTCAGTCCATCTCGAGCCTC

CGTTCAAAATGATCATCATCAAAACTTATGTGGCTTTTTGACCTTTGAATAGGG

CACCCAAAAAAAAAA

X-77

CCTCCCGGGCTCTTAAGCCCCTCTCTTTCTCTAACAGAAAAAGCGGATGGTGGT

TCCTGCTGCCCTCAAGGTCGTGCGTCTGAAGCCTACAAGAAAGTTTGCCTATCT GGGGCGCCTGGCTCACGAGGTTGGCTGGAAGTACCAGGCAGTGACAGCCACCCT

GGAGGAGAAGAGGAAAGAGAAAGCCAAGATCCACTACCGGAAGAAGAAACAGCT CATGAGGCTACGGAAACAGGCCGAGAAGAACGTGGAGAAGAAAATTGACAAATA CACAGAGGTCCTCAAGACCCACGGACTCCTGGTCTGAGCCCAATAAAGACTGTT AATTCCTCATGCGTTGCCTGCCCTTCCTCCATTGTTGCCCTGGAATGTACGGGA CCCAGGGGCAGCAGCAGTCCAGGTGCCACAGGCAGCCCTGGGACATAGGAAGCT

GGGAGCAAGGAAAGGGTCTTAGTCACTGCCTCCCGAAGTTGCTTGAAAGCACTC GGAGAATTGTGCAGGTGTCATTTATCTATGACCAATAGGAAGAGCAACCAGTTA CTATGAGTGAAAGGGAGCCAGAAGACTGATTGGAGGGCCCTATCTTGTGAGTGG GGCATCTGTTGGACTTTCCACCTGGTCATATACTCTGCAGCTGTTAGAATGTGC AAGCACTTGGGGACAGCATGAGCTTGCTGTTGTACACAGGGTATT

XI-13

CTGCCAACATGGTGTTCAGGCGCTTCGTGGAGGTTGGCCGGGTGGCCTATGTCT

CCTTTGGACCTCATGCCGGAAAATTGGTCGCGATTGTAGATGTTATTGATCAGA ACAGGGCTTTGGTCGATGGACCTTGCACTCAAGTGAGGAGACAGGCCATGCCTT TCAAGTGCATGCAGCTCACTGATTTCATCCTCAAGTTTCCGCACAGTGCCCACC AGAAGTATGTCCGACAAGCCTGGCAGAAGGCAGACATCAATACAAAATGGGCAG CCACACGATGGGCCAAGAAGATTGAAGCCAGAGAAAGGAAAGCCAAGATGACAG ATTTTGATCGTTTTAAAGTTATGAAGGCAAAGAAAATGAGGAACAGAATAATCA AGAATGAAGTTAAGAAGCTTCAAAAGGCAGCTCTCCTGAAAGCTTCTCCCAAAA

AAGCACCTGGTACTAAGGGTACTGCTGCTGCTGCTGCTGCTGCTGCTGCTGCTG CTGCTGCTGCTGCTGCTAAAGTTCCAGCAAAAAAGATCACCGCCGCGAGTAAAA AGGCTCCAGCCCAGAAGGTTCCTGCCCAGAAAGCCACAGGCCAGAAAGCAGCGC CTGCTCCAAAAGCTCAGAAGGGTCAAAAAGCTCCAGCCCAGAAAGCACCTGCTC CAAAGGCATCTGGCAAGAAAGCATAAGTGGCAATCATAAAAAGTAATAAAGGTT CTTTTTGACCTGTTAAAAAA

XI-49

GATCAACCTGGAGCTCTACGCCTCCTACGTTTACCTGTCCATGTCTTACTACTT TGACCGCGATGATGTGGCTTTGAAGAACTTTGCCAAATACTTTCTTCACCAATC TCATGAGGAGAGGGAACATGCTGAGAAACTGATGAAGCTGCAGAACCAACGAGG TGGCCGAATCTTCCTTCAGGATATCAAGAAACCAGACTGTGATGACTGGGAGAG

CGGGCTGAATGCAATGGAGTGTGCATTACATTTGGAAAAAAATGTGAATCAGTC ACTACTGGAACTGCACAAACTGGCCACTGACAAAAATGACCCCCATTTGTGTGA CTTCATTGAGACACATTACCTGAATGAGCAGGTGAAAGCCATCAAAGAATTGGG TGACCACGTGACCAACTTGCGCAAGATGGGAGCGCCCGAATCTGGCTTGGCGGA AT ATCTCTTTGACAAGCACACCCTGGGAGACAGTGATAATGAAAGCTAAGCCTC

GGGCTAATTTCCCCATAGCCGTGGGGTGACTTCCCTGGTCACCAAGGCAGTGCA TGCATGTTGGGGTTTCCTTTACCTTTTCTATAAGTTGTACCAAAACATCCACTT AAGTTCTTTGATTTGTACCATTCCTTCAAATAAAGAAATTTGGTACCC

XI-81

AGAGCAGCAGCCATGGCCCTACGCTACCCTATGGCCGTGGGCCTCAACAAGGGC CACAAAGTGACCAAGAACGTGAGCAAGCCCAGGCACAGCCGACGCCGCGGGCGT CTGACCAAACACACCAAGTTCGTGCGGGACATGATTCGGGAGGTGTGTGGCTTT GCCCCGTACGAGCGGCGCGCCATGGAGTTACTGAAGGTCTCCAAGGACAAACGG GCCCTCAAATTTATCAAGAAAAGGGTGGGGACGCACATCCGCGCCAAGAGGAAG

CGGGAGGAGCTGAGCAACGTACTGGCCGCCATGAGGAAAGCTGCTGCCAAGAAA GACTGAGCCCCTCCCCTGCCCTCTCCCTGAAATAAA .

XII-35 CTCTCCTGTCAACAGCGGCCAGCCTCCCAACTACGAGATGCTCAAGGAGGAGCA

GGAAGTGGCTATGCTGGGGGCGCCCCACAACCCTGCTCCCCCGACGTCCACCGT GATCCACATCCGCAGCGAGACCTCCGTGCCCGACCATGTCGTCTGGTCCCTGTT CAACACCCTCTTCATGAACACCTGCTGCCTGGGCTTCATAGCATTCGCCTACTC CGTGAAGTCTAGGGACAGGAAGATGGTTGGCGACGTGACCGGGGCCCAGGCCTA TGCCTCCACCGCCAAGTGCCTGAACATCTGGGCCCTGATTTTGGGCATCTTCAT

GACCATTCTGCTCGTCATCATCCCAGTGTTGGTCGTCCAGGCCCAGCGATAGAT CAGGAGGCATCATTGAGGCCAGGAGCTCTGCCCGTGACCTGTATCCCACGTACT CTATCTTCCATTCCTCGCCCTGCCCCCAGAGGCCAGGAGCTCTGCCCTTGACCT GTATTCCACTTACTCCACCTTCCATTCCTCGCCCTGTCCCCACAGCCGAGTCCT GCATCAGCCCTTTATCCTCACACGCTTTTCTACAATGGCATTCAATAAAGTGTA TATGTTTCTGGTGCTGCTGTGACTTCAA

GTAAGAAAGCCCTTAAATAAAGAAGGTAAGAAACCTAGGACCAAAGCACCCAAG ATTCAGCGTCTTGTTACTCCACGTGTCCTGCAGCACAAACGGCGGCGTATTGCT CTGAAGAAGCAGCGTACCAAGAAAAATAAAGAAGAGGCTGCAGAATATGCTAAA CTTTTGGCCAAGAGAATGAAGGAGGCTAAGGAGAAGCGCCAGGAACAAATTGCG

AAGAGACGCAGACTTTCCTCTCTGCGAGCTTCTACTTCTAAGTCTGAATCCAGT CAGAAATAAGATTTTTTGAGTAACAAATAAATAAGATCAGACTCTGAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

XIfl-29

CTCGCTCACGCAGCACTCGTGGCAGTCCCTGAAGGACCGCTACCTCAAGCACCT GCGGGGCCAGGAGCATAAGTACCTGCTGGGGGACGCGCCGGTGAGCCCCTCCTC CCAGAAGCTCAAGCGGAAGGCGGAGGAGGACCCGGAGGCCGCGGATAGCGGGGA ACCACAGAATAAGAGAACTCCAGATTTGCCTGAAGAAGAGTATGTGAAGGAAGA AATCCAGGAGAATGAAGAAGCAGTCAAAAAGATGCTTGTGGAAGCCACCCGGGA

GTTTGAGGAGGTTGTGGTGGATGAGAGCCCTCCTGATTTTGAAATACATATAAC TATGTGTGATGATGATCCACCCACACCTGAGGAAGACTCAGAAACACAGCCTGA TGAGGAGGAAGAAGAAGAAGAAGAAAAAGTTTCTCAACCAGAGGTGGGAGCTGC CATTAAGATCATTCGGCAGTTAATGGAGAAGTTTAACTTGGATCTATCAACAGT TACACAGGCCTTCCTAAAAAATAGTGGTGAGCTGGAGGCTACTTCCGCCTTCTT

AGCGTCTGGTCAGAGAGCTGATGGATATCCCATTTGGTCCCGACAAGATGACAT AGATTTGCAAAAAGATGATGAGGATACCAGAGAGGCATTGGTCAAAAAATTTGG TGCTCAGAATGTAGCTCGGAGGATTGAATTTCGAAAGAAATAATTGGCAAGATA ATGAGAAAAGAAAAAAGTCATGGTAGGTGAGGTGGTTAAAAAAAATTGTGACCA ATGAACTTTAGAGAGTTCTTGCATTGGAACTGGCACTTATTTTCTGACCATCGC TGCTGTTGCTCTGTGAGTCCTAGATT

Xiπ-84

ATTATCCTCAGTTCCCAAGAGCAATCATACTTTTCCACACATACCGTGTGTCTC ATGTTAGGTAAATGTATTTTTACAATGAGCACCACTTCTGTGGAAAAAGTTCCC

TGCACGGGGAGGTCCAGCTTCCAGACTGCTCCATCGCATAAGGACTTCCCCATT CCCCTAAATGCTGCTCTGTCAGAACCTGCCCAGGTAATGGTAATGACCCTAGAG AGATGATTTCTGAACCGCAATTTTGAGCCCATTAGAAGGTGTGTGGTGGGCATT TATTTCATCCTGATGCTCTGGTGAGAATCTTTGCAGACGCACTAGATCCAGAAG CTGTTAATCTTGGTGCATTTATTTTCCTACCTAAAAGAACCAAGCAGCTCAGAG GCAGTGACTGTACAGGATGCAGTGTTTATAATAATGCTGAGCTTGCTGGTCTGG AACCCCACACTTCAGCAATCCCAGCATTGTTCCTGTTTATGAAGTTGACAAAGT

GACCAGGGCAAGGGGGTATTATCATTAAATACACTCTAGGAGAGGCAGAACACA TGAGGGCAATGTTTTTCAGAGGTCTTTAGGCCACCGCATCAGATTCTCCTGGAG CATAAAGCAAATGCTTTATGAGTCCAGGGCCCCTGCAGACCTACTGTATACTAG TATACAGCTCCCTCTTAGTGGATCTCAAGCTTGTTTCCAAAAAGTCATTACACT CCTTACCAAAGCCCATGACACATTCATACAGATTCATCCAGACATAACCCACTG

CATGGTCCAGTGCATGCTTGTGTGCTTAACTTATTATAGATCAAGTGTTATTTA AGTCCAACATATTAAACGTGACTGAATATT

XN-49 AAGTCTGCCCAGAAAGCTCAGAAGGCTAAATGAAT ATTATCCCTAATACCTGCC

ACCCCACTCTTAATCAGTGGTGGAAGAACGGTCTCAGAACTGTTTGTTTCAATT GGCCATTTAAGTTTAGTAGTAAAAGACTGGTTAATGATAACAATGCATCGTAAA ACCTTCAGAAGGAAAGGAGAATGTTTTGTGGACCACTTTGGTTTTCTTTTTTGC GTGTGGCAGTTTTAAGTTATTAGTTTTTAAAATCAGTACTTTTTAATGGAAACA ACTTGACCAAAAATTTGTCACAGAATTTTGAGACCCATTAAAAAAGTTAAATGAG

XV-54

AAGAGCAGGTCTCTGGAGGCTGAGTTGCATGGGGCCTAGTAACACCAAGCCAGT

GAGCCTCTAATGCTACTGCGCCCTGGGGGCTCCCAGGGCCTGGGCAACTTAGCT GCAACTGGCAAAGGAGAAGGGTAGTTTGAGGTGTGACACCAGTTTGCTCCAGAA

AGTTTAAGGGGTCTGTTTCTCATCTCCATGGACATCTTCAACAGCTTCACCTGA CAACGACTGTTCCTATGAAGAAGCCACTTGTGTTTTAAGCAGAGGCAACCTCTC TCTTCTCCTCTGTTTCGTGAAGGCAGGGGACACAGATGGGAGAGATTGAGCCAA GTCAGCCTTCTGTTGGTTAATATGGTATAATGCATGGCTTTGTGCACAGCCCAG TGTGGGATTACAGCTTTGGGATGACCGCTT ACAAAGTTCTGTTTGGTT AGTATT

GGCATAGTTTTTCTATATAGCCATAAATGCGTATATATACCCATAGGGCTAGAT CTGTATCTTAGTGTAGCGATGTATACATATACACATCCACCTACATGTTGAAGG GCCTAACCAGCCTTGGGAGTATTGACTGGTCCCTTACCTCTTATGGCTAAGTCT TTGACTGTGTTCATTTACCAAGTTGACCCAGTTTGTCTTTTAGGTTAAGTAAGA CTCGAGAGTAAAGGCAAGGAGGGGGGCCAGCCTCTGAATGCGGCCACGGATGCC

TTGCTGCTGCAACCCTTTCCCCAGCTGTCCACTGAAACGTGAAGTCCTGTTTTG AATGCCAAACCCACCATTCACTGGTGCTGACTACATAGAATGGGGTTGAGAGAA GATCAGTTTGGGCTTCACAGTGTCATTTGAAAACGTTTTTTGTTTTGTTTTGTA ATTATTGTGGAAAACTTTCAAGTGAACAGAAGGATGGTGTCCTACTGTGGATGA GGGATGAACAAGGGGATGGCTTTGATCCAATGGAGCCTGGGAGGTGTGCCCAGA AAGCTTGTCTGTAGCGGGTTTTGTGAGAGTGAACACTTTCCACTTTTTGACACC TTATCCTGATGTATGGTTCCAGGATTTGGATTTTGATTTTCCAAATGTAGCTTG AAATTTCAATAAACTTTGCTCTGTTTTTCTAAAAATAAAAAAAAAAAAAAAAAA

AAAAAAAA

XV-75 AGCAGATGACCCTTCGTGGCACCCTCAAGGGCCACAACGGCTGGGTAACCCAGA TCGCTACTACCCCGCAGTTCCCGGACATGATCCTCTCCGCCTCTCGAGATAAGA

CCATCATCATGTGGAAACTGACCAGGGATGAGACCAACTATGGAATTCCACAGC GTGCTCTGCGGGGTCACTCCCACTTTGTTAGTGATGTGGTTATCTCCTCAGATG GCCAGTTTGCCCTCTCAGGCTCCTGGGATGGAACCCTGCGCCTCTGGGATCTCA CAACGGGCACCACCACGAGGCGATTTGTGGGCCATACCAAGGATGTGCTGAGTG TGGCCTTCTCCTCTGACAACCGGCAGATTGTCTCTGGATCTCGAGATAAAACCA

TCAAGCTATGGAATACCCTGGGTGTGTGCAAATACACTGTCCAGGATGAGAGCC ACTCAGAGTGGGTGTCTTGTGTCCGCTTCTCGCCCAACAGCAGCAACCCTATCA TCGTCTCCTGTGGCTGGGACAAGCTGGTCAAGGTATGGAACCTGGCTAACTGCA AGCTGAAGACCAACCACATTGGCCACACAGGCTATCTGAACACGGTGACTGTCT CTCCAGATGGATCCCTCTGTGCTTCTGGAGGCAAGGATGGCCAGGCCATGTTAT

GGGATCTCAACGAAGGCAAACACCTTTACACGCTAGATGGTGGGGACATCATCA ACGCCCTGTGCTTCAGCCCTAACCGCTACTGGCTGTGTGCTGCCACAGGCCCCA GCATCAAGATCTGGGATTTAGAGGGAAAGATCATTGTAGATGAACTGAAGCAAG AAGTTATCAGTACCAGCAGCAAGGCAGAACCACCCCAGTGCACCTCCCTGGCCT GGTCTGCΓGATGGCCAGACTCTGTTTGCTGGCTACACGGACAACCTGGTGCGAG

TGTGGCAGGTGACCATTGGCACACGCTAGAAGTTTATGGCAGAGCTTTACAAAT AAAAAAAAAACTGGCTTTTCTGACAAAAAAAAAA

XN-86 GCAAAATGTCGCAGCTGGAACGCAACATAGAGACCATCATCAACACCTTCCACC AATACTCTGTGAAGCTGGGGCACCCAGACACCCTGAACCAGGGGGAATTCAAAG AGCTGGTGCGAAAAGATCTGCAAAATTTTCTCAAGAAGGAGAATAAGAATGAAA AGGTCATAGAACACATCATGGAGGACCTGGACACAAATGCAGACAAGCAGCTGA GCTTCGAGGAGTTCATCATGCTGATGGCGAGGCTAACCTGGGCCTCCCACGAGA AGATGCACGAGGGTGACGAGGGCCCTGGCCACCACCATAAGCCAGGCCTCGGGG

AGGGCACCCCCTAAGACCACAGTGGCCAAGATCACAGTGGCCACGGCCACGGCC ACAGTCATGGTGGCCACGGCCACAGCCACTAATCAGGAGGCCAGGCCACCCTGCCT CTACCCAACCAGGGCCCCGGGGCCTGTTATGTCAAACTGTCTTGGCTGTGGG GCTAGGGK5CTGGGGCCAAATAAAGTCTCTTCCTCCAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAA

XNI-74 CGCCGCCGCGCCGCCGTCGCTCTCCAACGCCAGCGCCGCCTCTCGCTCGCCGAG CTCCAGCCGAAGGAGAAGGGGGGTAAGTAAGGAGGTCTCTGTACCATGGCTCGT ACAAAGCAGACTGCCCGCAAATCGACCGGTGGTAAAGCACCCAGGAAGCAACTG GCTACAAAAGCCGCTCGCAAGAGTGCGCCCTCTACTGGAGGGGTGAAGAAACCT CATCGTTACAGGCCTGGTACTGTGGCGCTCCGTGAAATTAGACGTTATCAGAAG TCCACTGAACTTCTGATTCGCAAACTTCCCTTCCAGCGTCTGGTGCGAGAAATT GCTCAGGACTTTAAAACAGATCTGCGCTTCCAGAGCGCAGCTATCGGTGCTTTG CAGGAGGCAAGTGAGGCCTATCTGGTTGGCCTTTTTGAAGACACCAACCTGTGT GCTATCCATGCCAAACGTGTAACAATTATGCCAAAAGACATCCAGCTAGCACGC CGCATACGTGGAGAACGTGCTTAAGAATCCACTATGATGGGAAACATTTCATTC TCAAAAAAAAAAAAAAAAAATTTCTCTTCTTCCTGTTATTGGTAGTTCTGAACG TTAGATATTTTTTTTCCATGGGGTCAAAAGGTACCTAAGTATATGATTGCGAGT GGAAAAATAGGGGACAGAAATCAGGTATTGGCAGTTTTTCCATTTTCATTTGTG TGTGAATTTTTAATATAAATGCGGAGACGTAAAGCATTAATGCAAGTTAAAATG TTTCAGTGAACAAGTTTCAGCGGTTCAACTTTATAATAATTATAAATAAACCTG TTAAATTTTTCTGGACAATGCCAGCATTTGGATTTTTTTAAAACAAGTAAATTT CTTATTGATGGCAACTAAATGGTGTTTGTAGCATTTTTATCATACAGTAGATTC CATCCATTCACTATACTTTTCTAACTGAGTTGTCCTACATGCAAGTACATGTTT TTAATGTTGTCTGTCTTCTGTGCTGTTCCTGTAAGTTTGCTATTAAAATACATT

AAACTATAAAAAAAAAAAAAAAAAAA

XNII-77 CAGACACCCTGAACCAGGGGGAATTCAAAGAGCTGGTGCGAAAAGATCTGCAAA ATTTTCTCAAGAAGGAGAATAAGAATGAAAAGGTCATAGAACACATCATGGAGG

ACCTGGACACAAATGCAGACAAGCAGCTGAGCTTCGAGGAGTTCATCATGCTGA TGGCGAGGCTAACCTGGGCCTCCCACGAGAAGATGCACGAGGGTGACGAGGGCC CTGGCCACCACCATAAGCCAGGCCTCGGGGAGGGCACCCCCTAAGACCACAGTG GCCAAGATCACAGTGGCCACGGCCACGGCCACAGTCATGGTGGCCACGGCCACA GCCACTAATCAGGAGGCCAGGCCACCCTGCCTCTACCCAACCAGGGCCCCGGGG

CCTGTTATGTCAAACTGTCTTGGCTGTGGGGCTAGGGGCTGGGGCCAAATAAAG TCTCTTCCTCCAAAAAAA

XLϊ-78 no sequence available Table 1. Sample detail. Stage 0, in situ carcinoma; Stage I, invasive carcinoma with tumour size < 20 mm; Stage II, invasive carcinoma with tumour size > 20-50 mm; Stage IE, invasive carcinoma with tumour size >50 mm. Stage IV, cancer spread to distant parts. IDC, invasive ductal carcinoma; DCIS, ductal carcinoma in situ; TLC, invasive lobular carcinoma. n.a., not available. ND, non-decision. *, Blood samples taken at five consecutive weeks from the same female

Subgroup A3: Women with no breast abnormality

Table 2 Details of the 35 significant genes selected by Jackknife. Their position in the array, clone ID is shown as well as the accession number of sequences in public databases that match them, and their known or putative cellular function.

UPREGU ATED GENES

Table 2 -^«»*"^>• Downre ulated enes

Table 3 : Informative probes for breast cancer - family (i) and (ii) genes

Probe Agilent ID Oligonucleotide sequence No. A 23 P164011 ACTCCAGACTGGGAAGACCTTTCCATTTTCAGGATCGACGCTTCACGTTGAGGGGAGGGC A 23 P94111 TTACCAAACTCAAAGCTTATTTGAGTAGAATGGGCTCATGGGCAATGTGATGTTCCCTGT A 23 P155009 TGTTGGTTGGAGGACAAGTGGGCACTGAGACCCTGGTGACCCATGGAAAGGGTGGGCCTG A 23 P84323 TGGAGAAAGGACCCTGGACCTGTGGGTCCATCGTCCGTTCCAGGAGCAGGCAGGCTGGGG A 23 P121716 TGGACATTCGAACAGAGTTCAAGAAGCATTATGGCTATTCCCTATATTCAGCAATTAAAT

10 A 23 P111037 ATCAGAAGTCCACTGAACTGCTTATTCGTAAACTACCTTTCCAGCGCCTGGTGCGCGAGA

13 A 23 P75830 TTTGTGGAAACTGTGTGTTATACTTTGTGGTATAGACTGCCTGTTTAGTATGAAGGGGCG

16 A 23 P149936 CCTCCCAGCAGTTAAGTAACTTGTGTGAAGATGGGACCCTTGTTCCTAATGGTTCTAGAA

17 A 23 P 134805 CTGAATCTGTTTTGTCTTCCTAATCTATCACAATTGCCACCCATCGGGTTTTGGGTGTGT

18 A 23 P 154235 CCATGTTTCTGAATCTTCTTTGTTTCAAATGGTGCTGCATGTTTTCAACTACAATAAGTG

19 A 23 P2616 ATCATTCAGAATCTGAAAAGAAATTCTTCTTATTTTCTGGGGCTGTCAGATCCAGGGGGT

20 A 23 P333484 CCACCGAGCTGCTGATCAGAAAGCTGCCTTTTCAGCGTCTGGTGCGTGAGATCGCGCAGG

24 A 23 P259874 TCTCAGAAGAATGTTGGCCATGAGACTATCATTCAGAGGAGGAGGGGATTTCTCTCTTCA

26 A 23 P206568 AATCCTGTGATTCTGTGTGTGCCTGTGTGTGTATGCTGTTAATAAGATAAGGCTGCCCAT

27 A 23 P115091 GATGGCTGAAGGAGCTCTATGACCATGCTGAAGCCACGATCGTCGTCATGCTCGTGGGTA

28 A 23 P46718 TGCATGGGGAGTACATTCATCTGGAGGCTGCGTCCTGATGAATGTCCTGTCTGCTGGGGT

29 A 23 P218456 GTTTTTGAGTTTTTGCAGTTCAGTATCCCTCTGTCTATTCACACTTCGTGTTAGTGGTAA

31 A 23 P76610 CAGTTTATGGATGTCTGGGCAATCATAGCACTTGCCATTTAAAAACATGCTACAGGGGCA

32 A 23 P206396 ATTATCAACTCACTGGTAACAACAGTATTCATGCTCATCGTATCTGTGTTGGCACTGATA

34 A 23 P56091 GAAACCGGATCGCAAGCTTCCCAGGATTCCTCTTCGTGCTGCTGGGGGTGGGAAGCATGG

35 A 23 P55184 TCAATTTCAAGGCCTCCCTGCCTCTACTAGGCGCCTTAGCTCACTATGGGGAACCACTTG

37 A 23 P 150974 CAAAATAGCTACATCCCTGAACACAGTCCGGAATATTACGGCCGGACCAGGGAATCGGGA

40 A 23 P111689 TTAATTCTATTGGCTCTTAGTCACTTGGAACTGATTAATTCTGACTTTCTGTCACTAAGC

41 A 23 P58937 GTCTCAAACAGCCGAAACCTGTCTTGCAATGGGGGGAGGGGGCGTTTCGCTTTCCTTCTT

42 A 23 P74828 TTGGCTTTTAGACATTATATATATTATCAGAGAAGTAGCCTAGTGGTCGTGGGGCACAGA

45 A 23 P42168 GGAACACTGTGAAAGTTACTTGGGGAGGGTGGGCCGGTGGGGCCGTAGCTCTCTACCTCT

47 A 23 P81278 TCAGACAGAGCTTGGTAAGTGACCCCTCTTAGAACTATTTCTCCTCAGGGCCGGGTCCAG

A 23 P251695 AGGTTGAACTCTTTTTTGTTGCTCAAGTTCTAGGAGTCCCTTTCCTGAATATATACTTGT

A 23 P393645 CTACTTTAGAGTCTTCTCCAATGTCCAAAAGGCTAGGGGGTTGGAGGTGGGGACTCTGGA

A 23 P208683 ATAGTCATGGGTGTCATGAAAAAATACCAAATGTAAGAGAACCTCCAAGTCAGGGCGCAG

A 23 P72016 GACATTGAGAAGGAAAACCGGGAGGTGGGAGACTGGCGCAAGAATATCGATGCACTAAGT

A 23 P16915 CATATTCCATTTTTAAGAAGAGGTGTTCCAGTTCTGCATCTGATACCGTCTCCTTTCCCT

A 23 P37076 GAAAATCCCTTGCTATGTCTTTCCTACTAGAAATGTTCTAGAATCGCTGGACGGTGGGGT

A 23 P 166408 TGGTGGTGGATCCTGGAATTTTCTCACGCAGGAGCCATTGCTCTCCTAGAGGGGGTCTCA

A 23 P94501 GGCTCTTTGTGGAGGAAACTAAACATTCCCTTGATGGTCTCAAGCTATGATCAGAAGACT

A 23 P94230 TGAAGCTATTTCTGGGAGCCCAGAAGAAATGCTCTTTTGCTTGGAGTTTGTCATCCTACA

A 23 P 154037 TTGGTTTCCTCTAGGGTGATATTCGTCATTACTCTGTCTCTTCAATCCATCCAGCTAAAT

A 23 P47938 AAGAAAACACACCTCGGCGACAATGTCTTGCTGCTCGGATTAGGTGGGGGATGGGCGACA

A 23 P90743 CTGAGTTTGCCTTGTTAATCTTCAATAGTTTTACCTACCCCAGTCTTTGGAACCCTAAAT

A 23 P171249 AAAGTGTCAAGTGATTAAGTGTGTATTTGTACCCTAGATGATATGAACCAGCAGTCTTGT

A 23 P 142675 TGAGCTGTTCCCTTCTCTAAGCCATAATCTCTTAGTGGATTGAGCCCTCTTGGAAAGACT

A 23 P 153637 TGTTATTGGCCTAGAGCTACACGTATATGGGTTTGTCCTGA3TCCGTTTTCAAATGACCT

A 23 P76749 CCTGTTCTGTTTTTGCTTTTCCTCTTCTTGACCAAAGCATGTGCCACTAGCTGTCCTTGA

A 23 P169061 TTGAAGGCAAAGATCATCAATATCTGCATCTGGCTGCTGTCGTCATCTGTTGGCATCTCT

A 23 P206253 CGAATTGGGAGGCTTATATTTTTCAGCAAAGAAATTTTGGGGGGTTTTGTGTTGTTGGGC

A 23 P151995 AATAAACAACTTTGATGATGTAACTTGACCTTCCAGAGTTATGGAAATTTTGTCCCCATG

A 23 P138011 AGAGACCTGCAGGGGCCTCGGCCCCTCACATCGTGTATGTCTCTCCTTGATTTGTGTTGT

A 23 P35912 GCCAAAGCTCAAATGCCCACCATAGAACGACTGTCCATGACAAGATATTTCTACCTCTTT

A 23 P99424 TGGCTCCCCATCATGTATCCTCCCGATTATTGCGTATTCTAAAATAGGAAACAAGACTTT

A 23 P418986 GATGACACTGCCACCTCTGACTTCTGCCTCTGGCCTTCCACTCTCAGTAAGAAGAGCCAG

Table 4: Informative probes for breast cancer - non-family (i) and (ii) genes

Probe Agilent ID No . A 23 P366812 TTTACTTCTACCTGCTCTTCCCCAACTCCCTGAGCCTGAGTGAGCGTGTGGCCATCATCA A 23 P389391 TGGGCCTCAAAATGGAGATGGATCCCAGGTCTTGTGGGACCCTGGGATGTTTGGGGACTT A 23 P4096 TAATATCCCCAAACCTG AG ATG AG CACTACG ATGG CAG AG AG CAG CCTGTTG G ACCTG CT A 23 P 15450 GACTGAAAAATCAGCTTTCTATTTACATGAAACACTTTGGGGGTCATGGGAGTGCACAGC

11 A 23 P379596 AGGGATAATTCAAACTGACAACCTGTGCAGTCCCGTGGAGGGTAGGGGAGTGTGGGTGAT

12 A 23 P391275 TAAATTATGATTTACTCTGTGCTGTTTCCAAATTGGGACCAGGAGAGAAATATGAACTTC

14 A 23 P124661 TCTATTATTTATAACTTCAGACTTGGGCCCCCTGTTCTTTCTTTCCCATTAACTTGAGTG

15 A 23 P44257 AACATTTTACTTCTGCGCTTCTATGTTTGGGAAACATTGCTCTGATAAAAAATAGCTGTC

21 A 23 P128183 CTGAGAGTTTTTGCAGAAATGGGGCAGAGGGACACCCTTTGGGCGTGGCTTCCTGGTGAT

22 A 23 P331211 CGAGTGGCTCACTCAGAATTCTTCATTGATGGGCTAGGGACCCTACTCGTGGGGTCATGC

23 A 23 P94932 TCTGTTGATGACCTTGGATGCTGTAAAGTGATTCGTCATAGTCTCTGGGGTACCCATGTA

25 A 23 P102122 AAG CGG CTG GCAACTGAAG GCTGG AACACTTGCTACTG GATAATCGTAGCTTTTAATGTT

30 A 23 P407654 GAGGAGCTCTTTTCTAGAGAGCCGGGAGTTGGGGAGGGGGTATTTATTTTGTTATTTATT

33 A 23 P392457 CCTCTGACTGCCTCCAACGTAAAAATGTAAATATAAATTTGGTTGAGATCTGGAGGGGGG

36 A 23 P406376 GCCACACTGGCTTTAGGACCTGTTGACACGGAGGGGGG1 ΓAATTTGGTTTTTAACAA

38 A 23 P22723 AACAAACTACAGTTTTACCGTGTGTTTGCCATTTGAGCTGTGTGGTGGGCAGGGGGCTGG

39 A 23 P70258 AGAGAGGATGGCTGTATTCCTATCCCAGCTCAAGCTGCCAGCAGCAATGTTGGCTGCCCA

43 A 23 P104005 AATTTTCAAGACTTCTTTTCACTCTTTGATTTGGATCTGGCAAATTGGGGAGGGGATGCT

44 A 23 P119652 TTGCCCAACTGACCGTGGGCTGAACACACGTTCTGCTTGACTCATTTAGGGGGGAGGGAA

46 A 23 P22957 ATGAGGTGATCACTGTGTTCAGTGTTGTTGGAATGGATTCAGACTGGCTAATGGGGGAAA

48 A 23 P8072 GGGGGAGATCAGAATCGTCCAGCTGGGCTTCGACTTGGATGCCCATGGAATTATCTTCAC

50 A 23 P23346 AATCTTCTGAACGGCATAAGTCCTATTTTAGCCTTACCTCCTGCATTTGCAATACGTAAT

51 A 23 P92342 CGAACAAACAAAATACTTGGCGGGGCCCGAGAGGGCTCGTTTGGCCTATTCGTTGGGGAT

54 A 23 P153183 ACAGAAAACAGACTTGTAAAAAGCTTAGATCATCAAGTGTTTTGGATTGGGGGCCTCCCA

56 A 23 P157231 TGCAGAATGCATAAGATGAACATTGCATGACCGGATCATTTTAGTGTCTTTGCGTTAAAA

57 A 23 P 103282 TGAAGATCATGAAGAAGCAGGGCCTCTACCTACAAAAGTGAATCTTGCTCATTCTGAAAT

59 A 23 P 109462 CTGGATGTTTACCTGGAGACCGAGAGCCATGACGACAGTGTGGAGGGGCCCAAGGAATTT

A 23 P395460 TTAATGCTTTATACTGCCGAGTCTGGGGGCTTGTTTTGGTTTGGGGGCAGCCATCCTCCA

A 23 P418485 TCTAGGACTAATTCACACTGCAACAAAGGGGCTGATTAGAGCTTTTGAAGATGGGGGGAT

A 23 P215111 GACTTAACCACGTCAGAGGAAGGACTTTGGCAAGTGATATTGTCTTCATGTGGGGTATTA

A 23 P 19543 CTGTCAAATTGCCACGATCTCACTAAAGGATTTCTATTTGCTGTCAGTTAAAAATAAAGC

A 23 P18317 TCATCTGCACTCAACATTTAATCGTGTCCTTGCTGTCTTTTTATTTTCCTTTTTGTTTGT

A 23 P89369 GCGGGAGGAGCGGCCGCTGATGGTGTTCAACGTCAAGTAGCGCCCGCGCAGGGCGGGGCA

A 23 P330561 CTGTCTCCCTGTTTGTGTAAACATACTAGAGTATACTGCGGCGTGTTTTCTGTCTACCCA

A 23 P206103 GAGAGTTTCTTTTAAATAATCAGCGGGTGTTGGTGATTTGTAGCCCTTCTGCCCTTAAAT

A 23 P98042 ATACTTTGTGAGTTCACCTGTCTTTATACTCAAAAGTGTCCCTTAATAGTGTCCTTGCCC

A 23 P 166453 ACCTTTGAATTTGCGGATGCTGAGGAGGATGATGAGGTCAAGGTGTGAGGGGCTGGGGCA

A 23 P432554 TATTAGACTATGTCATCAATTTTTGCAAAGGTAAATTTGACTTCCTTGAACGGCTCTCAG

A 23 P368028 AAATACTGGGTGGCTTGGTTTAGAGCTAATTGTAGTGGAAGCCTGCAAGGTTGAGGGGTG

A 23 P213334 ACTCACTATGGCCAGAAAGCAATCTTGTTTCTCCCCCTGCCAGTCTCTTCTGATTAAAGA

A 23 P102113 AACAAATATTTATTTTGCACTCTCTTTGCGGCACTCTGGGGGCGGTGGGGTGCGTGGGGG

A 23 P319682 CAAGTTGTCACTGGAGATGCGCGCGGACTTGGCCCAAAACGTGCTTCTCTGCGGTGGGTC

A 23 P104471 GATTTCCCTGACCCAATTCAGAGATTCTTTATGCAAAAGTGAGTTCAGTCCATCTCTATA

A 23 P118749 GAAGGACTCGGTGATACCCACTGGGATCTTTTATCCTTTGTTGCAAAAGTGTGGACACTT

A 23 P420879 CAGGGCAACTCAAAGAATGTTCTGCTGGCATGTCCTATGAACATGTACCCGCATGGACGC

A 23 P29816 GAGAAAAGCAAAGCTCTTTCTTATTTTCCTCATAATCAGCTACCCTGGAGGGGAGGGAGA

A 23 P41992 TGAAATGCTGGAAGGGTTCTTCTCCCACAACCCCTGCCTCACGGAGGCCATTGCAGCTAA

A 23 P75479 AGACCTCGGTGATCACTGAGGGATTTCCGCGAGCTCGGCCTCACTTCTGCCCCGACTTGT

A 23 P307940 CTACAAGATTGGCAAAGAGATGCAGAATGCATAAGATGAACATTGCATGACCGGATCATT

A 23 P98910 AGGTTCTCAGAATGACCGTAAGATAGCTTACATTTCCTCTTTTTGCCTTTATCTCCCCAA

A 23 P 149736 CCG1 ΓGTTTCTGCTCAGTAATATAGTCAAGCAAGTTTGTTCCAAGTGACCCATTGAGC

A 23 P320250 AAATTGGCGCTGGAATTTGGGCTGGGAAAAATCTTGTGGTTATTTCCTTTAAAAAGGAAC

A 23 P55123 TCACGTTAACATATAGACACTGTTGGAAGCAGTTCCTTCTAAAAGGGTAGCCCTGGACTT

A 23 P251825 CTATGACACCTTTAAGGAGGTTCTTGGATCAGGGATGCAGTACCCACTTGCAGTCAAAAT

A 23 P109864 TGTGGGTGTCCAGCATCTTCTTCTTCCTTCCTGTCTTCTGTCTCACGGTCCTCTACAGTC

A 23 P428875 GTCGCCTGGGATTTTCATCCCTCGCACAAGGACTACGGGTTCACACGGTGAACTGGGGGA

A 23 P73468 GCCATAAGAAATTTGACAAGATGGTGGACACTCCTGCCTCCGAGCCTGCCCAAGCCTCCA

A 23 P 106532 AAGGCCTTTGAGGTTGTGACTGTGGCTGGTATATCTGGCTGCCATTTTTCTGATGCATTT

109 A 23 P112251 AGAATTCTTAACTTCACAAGTGTTTTACTTCGACGATGTGCCTTTGATTTAATTTGGGAC

110 A 23 P313330 TCATTAGACATCGGGGATTTCACTCTGCAGAGTAATCCTGGAACTACATTAAAGTGGGGG

111 A 23 P27414 TGCGGGAAGCCTTTCAGCCACCGTTGCAACCTCAACGAGCACCAGAAGCGGCACGGGGGC

112 A 23 P210981 TTGTAGGACTTAATGGCTAAGAATTAGAACATAGCAAGGGGGCTCCTCTGTTGGAGTAAT

Table 5 : Informative genes for breast cancer - family (i) and (ii) genes

Accession numbers 1 and 2 provide alternative accession numbers for the gene. The relevant sequence may be identified in the NCBI database (www.ncbi.nlm.nih.gov).

Table 6: Informative genes for breast cancer - non-family (i) and (ii) genes

Accession numbers are as defined in Table 5

Claims

Claims:

1. A method of preparing a standard gene transcript pattern characteristic of a cancer or stage thereof in an organism comprising at least the steps of: a) isolating mRNA from the cells of a sample of one or more organisms having the cancer or stage thereof, which may optionally be reverse transcribed- to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotide probes specific for said cancer or stage thereof in an organism and sample thereof conesponding to the organism and sample thereof under investigation, wherein said set comprises at least 10 oligonucleotides, wherein each oligonucleotide is selected from an oligonucleotide corresponding to a gene sequence from: family (i) genes encoding proteins involved in protein synthesis and/or stability; or family (ii) genes encoding proteins involved in the regulation of defence and/or chromatin remodelling; or derived from such a sequence, or an oligonucleotide with a complementary sequence, or a functionally equivalent oligonucleotide; and c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce a characteristic pattern reflecting the level of gene expression of genes to which said oligonucleotides bind, in the sample with the cancer or stage thereof.

2. A method of preparing a test gene transcript pattern comprising at least the steps of: a) isolating mRNA from the cells of a sample of said test organism, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotides as defined in claim 1 specific for a cancer or stage thereof in an organism and sample thereof conesponding to the organism and sample thereof under investigation; and c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce said pattern reflecting the level of gene expression of genes to which said oligonucleotides bind, in said test sample.

3. A method of diagnosing or identifying or monitoring a cancer or stage thereof in an organism, comprising the steps of: a) isolating mRNA from the cells of a sample of said organism, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotides as defined in claim 1 specific for said cancer or stage thereof in an organism and sample thereof corresponding to the organism and sample thereof under investigation; c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce a characteristic pattern reflecting the level of gene expression of genes to which said oligonucleotides bind, in said sample; and d) comparing said pattern to a standard diagnostic pattern prepared according to the method of claim 1 using a sample from an organism corresponding to the organism and sample under investigation to determine the presence of said cancer or a stage thereof in the organism under investigation.

4. A method as claimed in any one of claims 1 to 3 wherein said family (i) genes encoding proteins involved in protein synthesis and/or stability are selected from the group consisting of: (a) genes encoding ribosomal proteins and ribosomal activation proteins, preferably ribosomal proteins L1-L56, L7A, L10A, L13A, L18A, L23A, L27A, L35A, L36A, L37A, PO, PI, P2, S2-S29, S31, S33 -S36, S3A, S15A, S18A, S18B, S18C, S27A, 63, 115 (and pseudogenes), ribosomal protein kinases, ribonucleases, putative SI RNA binding domain protein, eukaryotic translation initiation factors and guanine nucleotide binding protein G; (b) genes encoding translation inhibition and initiation factors, preferably eukaryotic translation elongation factors, tRNA synthetases, RNA binding proteins, polyadenylation element binding proteins, tyrosine phosphatases, eukaryotic translation initiation factors, and RNA polymerase I, IE transcription factors; and

(c) genes encoding other modulators of transcription or translation, preferably cyclin D-type binding protein and guanine nucleotide binding protein.

5. A method as claimed in any one of claims 1 to 4 wherein said family (ii)^* genes encoding proteins involved in the regulation of defence and/or chromatin remodelling family are selected from the group consisting of:

(a) genes encoding immune response related proteins, preferably T-cell receptor and associated components, various cytokines, interferon regulatory factors, oncostatin M, Leukemia inhibitory factor, chemokine ligand and receptor family, complement components, interferon stimulated factors, MHC class I or II (or related components), adhesion proteins, nuclear factor of kappa polypeptide gene enhancer in B-cells, myelin basic protein, cathepsin, toll-like receptor, proteosome subunits, ferritin, protein kinases or phosphatases as well as their activators and inhibitors, leukocyte immunoglobulin-like receptor, immunoglobulin components, defensin, oxytocin, SI 00 calcium binding protein, lectin and its receptor and superfamily, leptin, phospholipase and growth factors;

(b) genes encoding TNF-induced proteins, preferably TNF alpha-induced protein 8, integrin, inhibitor of kappa light polypeptide gene enhancer in B-cells, TNF- associated factor 2, 5, nuclear factor of kappa light polypeptide gene enhancer in B-cells, MAP kinases, protein kinase C, ubiquitous kinase, cadherin, caspase, cyclin Dl, superoxide dismutase and interleukins;

(c) genes encoding hypoxia-induced proteins, preferably sestrin, El A binding protein p 00, endothelin, ataxia telangiectasia and Rad3 related protein, hexokinase 2, TEK tyrosine kinase, DNA fragmentation factor, caspase, plasminogen activator, hypoxia-inducible factor 1 and glucose phosphate isomerase;

(d) genes encoding oxidative stress proteins, preferably superoxide dismutase, glutathione synthetase, catalase, lactoperoxidase, thyroid peroxidase, myeloperoxidase, eosinophil peroxidase, oxidation resistance 1, peroxiredoxin, cytochrome P450, scavenger receptor, paraoxonase, glutathione reductase, NAD(P)H dehydrogenase, glutathione S-transferase, catenin, glutaredoxin, heat shock proteins, mitogen-activated protein kinases, enolase, thioredoxin reductase and peroxiredoxin; and

(e) genes encoding proteins involved in chromatin remodelling, preferably histone replacement proteins .

6. A method as claimed in claim 5, wherein:

(i) said cytokines are the interleukins or their receptors (preferably TL-1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17, 18, 20, 22 or 24) or tumour necrosis factor or its receptor or its superfamily (preferably TNF superfamily members 2, 3, 4, 5,

6, 7, 8, 9, 11, 12, 13, 14 or 15); and/or

(ii) said adhesion proteins are CDIA, CDIC, CDID, CD3Z, 6, 8, 11, 14, 18, 24, 27, 28, 29, 40, 44, 50, 54, 59, 74, 79B, 80, 81, 83, 86, 96 orlCAM; and/or (iii) said immunoglobulin components are heavy chain or Fc fragments, preferably of IgG, IgE or IgA or their superfamily; and/or

(iv) said growth factors are endothelial cell growth factor or erythropoietin.

7. A method as claimed in any one of claims 1 to 6 wherein said immune response proteins encoded by family (ii) genes are adhesion proteins, interleukins, their receptors or superfamily, TNF its receptor or superfamily, immunoglobulin components or erythropoietin.

8. A method as claimed in any one of claims 1 to 7 wherein the genes encoding the (i) families are down-regulated in cancer versus normal patients and in the case of (ii) families the encoding genes are up-regulated.

9. A method as claimed in any one of claims 1 to 8 wherein said probes conespond to genes which are systemically affected by said cancer or stage thereof.

10. A method as claimed in any one of claims 1 to 9 wherein said genes are constitutively moderately or highly expressed.

11. A method as claimed in any one of claims 1 to 9 wherein said set comprises a combination of oligonucleotides from family (i) and family (ii).

12. A method as claimed in any one of claims 4 to 11 wherein said set of oligonucleotides includes oligonucleotides from family (i)a, (ii)a and (ii)e.

13. A method as claimed in any one of claims 1 to 12 wherein said set includes oligonucleotides from genes encoding one or more ribosomal proteins and optionally one or more histones and optionally fenitin.

14. A method as claimed in any one of claims 1 to 13 wherein each oligonucleotide probe is selected from the oligonucleotides listed in Table 2 or 3, or derived from a sequence described in Table 2 or 3, or a complementary sequence thereof.

15. A method as claimed in claim 14 wherein each oligonucleotide probe is selected from the oligonucleotides listed in Table 2, or derived from a sequence described in Table 2, or a complementary sequence thereof.

16. A method as claimed in claim 14 wherein each oligonucleotide probe is selected from the oligonucleotides listed in Table 3, or derived from a sequence described in Table 3, or a complementary sequence thereof.

17. A method as claimed in any one of claims 14 to 16 wherein said set additionally comprises one or more oligonucleotide probes selected from the oligonucleotides listed in Table 4, or derived from a sequence described in Table 4, or a complementary sequence thereof.

18. A method as claimed in any one of claims 14 to 17 wherein said derived oligonucleotide of Table 2, 3 or 4 is a part of a gen e described by its Accession number in Table 2, 5 or 6, respectively, or a complementary sequence thereof.

19. A method as claimed in any one of claims 1 to 18 wherein said set consists offrom 10 to 500 probes.

20. A method as claimed in any one of claims 1 to 19 wherein said set of probes are irnmobilized on one or more solid supports.

21. A method as claimed in any one of claims 1 to 20 wherein said cells are not disease cells, have not contacted disease cells and do not originate from the site of disease.

22. A method as claimed in any one of claims 1 to 21 wherein said sample is obtained from a site distant to the site of disease.

23. A method as claimed in any one of claims 1 to 22 wherein said sample is tissue, body fluid or body waste.

24. A method as claimed in claim 23 wherein said sample is peripheral blood.

25. A method as claimed in any one of claims 1 to 24 wherein said cancer is stomach, lung, breast, prostate gland, bowel, skin, colon or ovary cancer, preferably breast cancer.

26. A method as claimed in any one of claims 1 to 25 wherein said organism is a mammal, preferably a human.

27. A method as claimed in any one of claims 1 to 26 wherein at least one of said probes in said set is suitable for diagnosing, identifying or monitoring at least two of said cancers or stages thereof.

28. A method of diagnosis or identification or monitoring as claimed in any one of claims 3 to 27 for the diagnosis, identification or monitoring of two or more cancers or stages thereof in an organism, wherein said test pattern produced in step c) of the diagnostic method is compared in step d) to at least two standard diagnostic patterns prepared as defined in any one of claims 1 or 4 to 27, wherein each standard diagnostic pattern is a pattern generated for a different cancer or stage thereof.

29. A set of oligonucleotide probes as defined in any one of claims 1 to 28.

30. A kit for performing a method as claimed in any one of claims 1 to 28 comprising a set of oligonucleotide probes as defined in claim 29 immobihzed on one or more solid supports.

31. A kit as claimed in claim 30 additionally comprising a package insert detailing how the method should be performed.

32. The use of a set of oligonucleotide probes or a kit as defined in any one of claims 29 to 31 to determine the gene expression pattern of a cell which pattern reflects the level of gene expression of genes to which said oligonucleotide probes bind, comprising at least the steps of: a) isolating mRNA from said cell, which may optionally be reverse transcribed to cDNA; b) hybridizing the mRNA or cDNA of step (a) to a set of oligonucleotide probes or a kit as defined in any one of claims 29 to 31 ; and c) assessing the amount of mRNA or cDNA hybridizing to each of said probes to produce said pattern.

33. A method of preparing a standard gene transcript pattern characteristic of a cancer or stage thereof in an organism comprising at least the steps of: a) releasing target polypeptides from a sample of one or more organisms having the cancer or stage thereof; b) contacting said target polypeptides with one or more binding partners, wherein each binding partner is specific to a marker polypeptide (or a fragment thereof) encoded by the gene to which an oligonucleotide as defined in any one of claims 1 to 27 binds, to allow binding of said binding partners to said target polypeptides, wherein said marker polypeptides are specific for said cancer in an organism and sample thereof conesponding to the organism and sample thereof under investigation; and c) assessing the target polypept de binding to said binding partners to produce a characteristic pattern reflecting the level of gene expression of genes which express said marker polypeptides, in the sample with the cancer or stage thereof.

34. A method of preparing a test gene transcript pattern comprising at least the steps of: a) releasing target polypeptides from a sample of said test organism; b) contacting said target polypeptides with one or more binding partners, wherein each binding partner is specific to a marker polypeptide (or a fragment thereof) encoded by the gene to which an oligonucleotide as defined in any one of claims 1 to 27 binds, to allow binding of said binding partners to said target polypeptides, wherein said marker polypeptides are specific for said cancer in an organism and sample thereof conesponding to the organism and sample thereof under investigation; and c) assessing the target polypeptide binding to said binding partners to produce a characteristic pattern reflecting the level of gene expression of genes which express said marker polypeptides, in said test sample.

35. A method of diagnosing or identifying or monitoring a cancer or stage thereof in an organism comprising the steps of: a) releasing target polypeptides from a sample of said organism; b) contacting said target polypeptides with one or more binding partners, wherein each binding partner is specific to a marker polypeptide (or a fragment thereof) encoded by the gene to which an oligonucleotide as defined in any one of claims 1 to 27 binds, to allow binding of said binding partners to said target polypeptides, wherein said marker polypeptides are specific for said cancer in an organism and sample thereof corresponding to the organism and sample thereof under investigation; and c) assessing the target polypeptide binding to said binding partners to produce a characteristic pattern reflecting the level of gene expression of genes which express said marker polypeptides in said sample; and d) comparing said pattern to a standard diagnostic pattern prepared according to the method of claim 33 using a sample from an organism corresponding to the organism and sample under investigation to determine the degree of correlation indicative of the presence of said cancer or a stage thereof in the organism under investigation.